Skip to main content
Glama
Sugra-Systems

sugra-api-mcp

Official

fetch_data

Read-onlyIdempotent

Searches and calls the matching Sugra API endpoint for your query, returning data directly without manual selection.

Instructions

One-step fetch: find the best Sugra endpoint for the query and call it.

Combines search_endpoints + call_endpoint into a single round trip. Use this when you want data without manually picking an operation_id. The full search_endpoints + describe_endpoint + call_endpoint dance is still available when you need explicit control, but for most natural-language queries this tool is enough.

Behavior:

  1. Search the bundled catalog for the query. Top match wins.

  2. If the matched endpoint has required parameters and they are all provided in params, call it and return the response.

  3. If required parameters are missing, return the candidate endpoints and the missing-params list so the LLM can retry with the correct params dict on the next call.

Examples:

  • fetch_data("US CPI inflation", params={"series_id": "CPIAUCSL"}) → calls /api/v1/fred/series/CPIAUCSL, returns observations.

  • fetch_data("Bitcoin price", params={"coin_id": "bitcoin"}) → calls /api/v1/crypto/bitcoin/price.

  • fetch_data("Latest financial news") → news_latest has no required params, returns latest news directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoJSON body for an auto-selected POST operation; the tool returns the request_body_schema to fill when the match needs one.
limitNo
queryYes
fieldsNo
paramsNoParameters for the auto-selected endpoint. If omitted and the best-match endpoint has required parameters, the tool returns that endpoint's required_parameters and examples so you can retry with them filled in.
include_rawNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already show readOnlyHint, destructiveHint, etc. Description adds step-by-step behavior (search, parameter check, decision), error handling, and examples, providing rich context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, well-structured with headers, bullet points, and examples. Every sentence adds value; no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity (combining search and call), the description covers main flow, error handling, and examples. Output schema exists, so return values are covered. Missing some parameter details, but overall complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 33%, so description should compensate. It explains the 'params' parameter and gives examples, but does not detail 'limit', 'fields', 'include_raw'. Could be more thorough but adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool combines search and call into one step, distinguishing it from siblings like search_endpoints and call_endpoint. The verb 'fetch' and resource 'Sugra endpoint' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('for most natural-language queries') and when to use alternatives ('when you need explicit control'). Provides examples for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Sugra-Systems/prod-sugra-ai-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server