Agents are good at reasoning and bad at remembering how to talk to every data vendor. The usual pattern is a growing pile of one-off integrations: one client for markets, another for macro series, another for sanctions lists, another for weather. Each has its own auth, schema, rate limit, and failure mode. The agent either hardcodes a few endpoints or invents numbers when a call fails.
This post is about a different shape: one MCP server in front of one API that already normalizes a large catalog of world data. The server is Sugra MCP (pip install sugra-api-mcp). Behind it sits the Sugra API - 1,500+ endpoints across 36 domains, backed by 160+ primary sources (sovereign statistics agencies, central banks, intergovernmental bodies, academic sources, and a smaller commercial slice under Sugra-branded wrappers).
The problem agents actually hit
Tool-using agents do not need "more models." They need reliable access to facts with provenance. In practice teams rebuild the same glue:
- Auth headers and key rotation per vendor
- Divergent JSON shapes and pagination
- Silent empty responses that the model fills with guesses
- No single place to discover what is even available
MCP fixes the client-side contract (tools, prompts, resources over one protocol). It does not by itself fix the data plane. You still need a stable catalog, a consistent call shape, and errors the agent can act on.
What MCP solves - and what it does not
MCP gives you:
- A standard way for Claude, ChatGPT, Gemini, xAI, Cursor, VS Code, and other clients to list and call tools
- Hosted HTTP and local stdio transports
- Room for prompts and resources next to tools
MCP does not give you:
- A curated multi-domain data catalog
- Source attribution and freshness on every number
- One rate limit and one API key across domains
Those belong in the data layer behind the server. Sugra MCP is that layer's agent face, not a replacement for the protocol.
Bundled catalog instead of live schema scraping
Discovery should not cost quota and should not depend on a network hop to the API for every search or describe.
The package ships a bundled endpoint catalog. Search and describe run locally against that catalog. Only real data fetches hit sugra.ai. That keeps agent planning loops cheap and makes the server useful offline for "what exists?" questions.
When the API grows, the catalog is regenerated and released with the package (and mirrored on the hosted endpoint after deploy). Agents get a versioned view of the surface instead of scraping OpenAPI on every session.
The search / describe / call pattern
The tool surface is intentionally small:
- Search - natural language or keywords over the catalog
- Describe - parameters, domains, and source notes for an endpoint
- Call / fetch - execute with structured arguments
On the hosted endpoint the surface is wider (11 tools vs 8 local), including composed helpers for common agent paths. The idea is the same: the agent finds the right operation, inspects it, then calls it - instead of you hardcoding paths into the system prompt.
Six workflow prompts (market snapshot, macro briefing, sanctions screening, sector compare, earth/conditions-style flows, source overview) package multi-step patterns for clients that surface MCP prompts. Three read-only catalog resources expose discovery material without burning data quota.
Structured errors instead of exceptions the model swallows
When an endpoint is missing, a parameter is wrong, or a source is temporarily empty, the tool returns a structured error the agent can branch on: retry, pick another series, or tell the user the data is unavailable.
That is more valuable than "covering every source on day one." An honest failure with a machine-readable reason beats a fluent wrong number.
Attribution and freshness as first-class fields
Every successful data result carries:
- Source attribution - where the number came from (sovereign and intergovernmental names are shown as-is; commercial upstreams appear under Sugra product wrappers on public surfaces)
- Freshness - how current the observation is
Agents can cite sources in the user-facing answer. Humans can audit a chain of tool calls. This is the difference between a demo scraper and something you can put near a research or compliance workflow.
Hosted vs local: one contract
| Mode | How | Tools | Best for |
|---|---|---|---|
| Local / self-hosted |
pip install sugra-api-mcp over stdio or your HTTP |
8 tools + prompts/resources | IDEs, private runners, offlin |
| Hosted | app.sugra.ai/mcp (Streamable HT |
Same API key model (SUGRA_API_KEY / account key). Free tier is 50 requests/day so you can evaluate without a card. Paid tiers scale volume only - the full endpoint surface is on every plan.
The server is also listed in the official MCP Registry as ai.sugra/api-mcp and in the OpenAI Plugins Directory for ChatGPT and Codex. On Smithery: sugra-systems/sugra-api.
Proof (live)
| Claim | Check |
|---|---|
| 1,500+ endpoints / 36 domains / 160+ sources | sugra.ai/stats |
| MCP package | PyPI · GitHub |
| Official MCP Registry | ai.sugra/api-mcp |
| Hosted MCP | app.sugra.ai/mcp |
| OpenAI Plugins | listing |
| Smithery | @sugra-systems/sugra-api |
| Free tier | 50 req/day - register |
MIT license. Every tool result carries source attribution and freshness - not a black box.
What is next
- Keep the catalog honest as domains grow
- Dual-host OAuth hardening for
mcp.sugra.aialongsideapp.sugra.ai/mcp - Tighter agent evals for search ranking and entity resolution
- More runnable recipes in the public cookbook
If you build research, compliance, or multi-domain agent workflows and you are tired of re-wiring vendors:
bash
pip install sugra-api-mcp
• Repo: github.com/Sugra-Systems/sugra-api-mcp (https://github.com/Sugra-Systems/sugra-api-mcp?utm_source=devto&utm_medium=article&utm_campaign=mcp_launch_202607)
• API and free key: sugra.ai (https://sugra.ai?utm_source=devto&utm_medium=article&utm_campaign=mcp_launch_202607)
• Hosted MCP: app.sugra.ai/mcp (https://app.sugra.ai/mcp?utm_source=devto&utm_medium=article&utm_campaign=mcp_launch_202607)
Feedback on the tool surface - especially from people wiring external data into coding agents - is welcome.
Top comments (2)
The distinction between "MCP fixes the client contract" and "MCP does not fix the data plane" is the right one. A lot of teams get tool discovery working and then still hand the model inconsistent error shapes, missing provenance, and rate-limit behavior it can't reason about. Treating freshness, attribution, and structured failures as first-class fields matters as much as the protocol itself. It also makes downstream traces far more debuggable when an agent picks the right tool but still reaches the wrong conclusion.
The bundled catalog is a strong latency/quota tradeoff. The main contract I would make explicit is catalog-to-runtime compatibility. If search/describe comes from package version N while the hosted API has moved to N+1, the worst failure is not a 404—it is a call that still succeeds with changed defaults, units, or semantics.
Each describe/call result could carry catalog_version, API schema/version, effective parameters, units, source observation timestamp, and retrieval timestamp. The server can then fail closed (or warn structurally) when the local catalog falls outside the API's supported compatibility window. Contract tests should replay representative endpoints across local and hosted modes and compare normalized result envelopes. A versioned catalog is useful; a version bound to the data-plane behavior is what makes the provenance chain auditable.