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Glama

Intact

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. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, destructiveHint=false. Description adds details about return value: 'top-N most relevant tools with names, descriptions, and full input schemas' and 'each result is ready to call directly, no second schema lookup needed.'

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

Conciseness4/5

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

The description is front-loaded with purpose and usage, but it runs long. Every sentence adds value, though minor trimming could improve conciseness.

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 no output schema, the description explains what is returned (top-N tools with full schemas). It covers the main behavior but does not specify empty query handling or ranking details.

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

Parameters4/5

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

Schema description coverage is 100% with all parameters described. Description adds context by listing aliases (task, q, search, description) and providing examples of query values, adding value beyond the schema.

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 states 'Find tools by describing the data or task,' a specific verb+resource. It distinguishes itself from sibling tools (e.g., deep_research, entity_profile) as the discovery/search tool among many specialized tools.

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

Usage Guidelines4/5

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

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set.' Provides use cases like 'browse, search, look up, or discover what tools exist for: ...' but does not explicitly state when not to use.

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

A4/5.0
Disambiguation3/5

Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research form a confusing cluster—especially since ask_pipeworx_beta is currently identical to ask_pipeworx. The Polymarket tools are highly specialized and mostly separable, and interaction_count/find_interactions have clear but overlapping scopes.

Naming Consistency4/5

The dominant pattern is verb_noun snake_case (ask_pipeworx, compare_entities, resolve_entity, validate_claim), which is predictable and readable. There are some noun-style names like entity_profile, recent_changes, and interaction_count, plus brand-prefixed families like pipeworx_* and polymarket_*, but the conventions are consistent enough within families.

Tool Count2/5

33 tools is beyond the 25+ threshold and the set spans several unrelated domains—molecular interactions, npm dependency scanning, llms.txt generation, AI brand visibility, and prediction-market arbitrage—making it feel like multiple servers merged into one. Several niche tools could be consolidated or split into separate MCP servers, and ask_pipeworx_beta adds redundancy.

Completeness4/5

Core workflows are very well covered: lookup/grounded answering/deep research, tool discovery, entity resolution, profiles and comparisons, claim validation, memory CRUD, subscription lifecycle, and prediction-market analysis from edge detection to fill-risk. Minor gaps include no direct fetch tool for pipeworx:// citation URIs and some soft-failing data sources, but agents can work around those.

Resources