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Glama

Data Gov In

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • addedInput schema / properties / description
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / q
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • changedInput schema / properties / query / description
      Previous value: -"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\")"New value: +"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\"). Accepts task, q, description, search as aliases."
    • addedInput schema / properties / search
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / task
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds key behavioral context: it returns tool metadata (names, descriptions, full input schemas) rather than actual data, and each result is 'ready to call directly, no second schema lookup needed'. This goes beyond annotations by explaining the output nature, though it omits details like ranking criteria or error behavior.

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 appropriately sized for a meta-tool. It front-loads 'Find tools by describing the data or task', then provides a relevant domain list, return-behavior details, and strategic usage advice. Every sentence contributes value, and the list of domains is useful, not padding. Structure is clear and scannable.

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

Completeness5/5

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

Despite having no output schema, the description fully explains what the tool returns: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)'. It also covers the default/max limit via the 'limit' parameter in schema. Given the tool's meta-purpose, the description is complete: it tells the agent what this discovery tool returns and when to invoke it.

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 description coverage is 100% – all 6 parameters (query, q, task, search, description, limit) are described with aliases and examples. The description text reinforces that the query is a natural-language description, but adds no additional semantic information beyond what the schema already provides. Baseline 3 is appropriate.

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 opens with 'Find tools by describing the data or task' – a clear verb+resource+method. It explicitly lists the domains covered (SEC filings, FDA, FRED, etc.) and distinguishes itself from siblings as the discovery/meta-tool. The phrase 'Call this FIRST...' further separates it from domain-specific tools.

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?

Provides explicit when-to-use guidance: 'Use when you need to browse, search, look up, or discover what tools exist...' and strategic instruction 'Call this FIRST when you have many tools available...'. This is strong usage direction, clarifying when to use discovery vs. directly calling a specific tool.

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

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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions. The six Polymarket/prediction-market tools also blur together, and ai_visibility_check versus scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Snake_case is used consistently, and most tools follow a verb-first or domain-prefixed pattern (ask_pipeworx, compare_entities, subscribe, polymarket_*). Minor deviations like entity_profile, resource_data, and ai_visibility_check are noun-first, but nothing is chaotic or mixed-cased.

Tool Count2/5

33 tools is excessive for a server named 'Data Gov In' whose actual domain-specific surface is only resource_data and resource_meta. The rest are generic Pipeworx, prediction-market, memory, and utility tools that do not belong to the apparent India open-data scope.

Completeness1/5

For a data.gov.in server, the surface is severely incomplete: there is no way to search or list datasets/resources, only fetch metadata and data for a known resourceId. The overwhelming majority of tools serve unrelated domains, so an agent using this server for Indian government data will hit dead ends immediately.

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