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Glama

Pride

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

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context about output: returns top-N tools with full schemas and curated examples, and that results are directly callable without a second lookup. This enriches understanding 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?

Three sentences, each serving a distinct purpose: defining the action, specifying when to use it, and describing the output. Front-loaded with the primary verb and resource, no wasted words.

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 no output schema, the description comprehensively covers what the tool returns (names, descriptions, schemas, examples), supports top-N/limit behavior, and clarifies that results are immediately usable. This is sufficient for a discovery tool.

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%, and the schema already explains the query parameter and aliases. The description reinforces natural-language usage with examples and lists domains, but adds minimal semantic 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 clearly states 'Find tools by describing the data or task' and enumerates numerous specific domains, making it obvious this is the tool-discovery meta-tool. It distinguishes itself from sibling tools by focusing on browsing/searching the toolset rather than performing a specific task.

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 instruction: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available'. This tells the agent exactly when to invoke it versus directly calling a specialized 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

A3.9/5.0
Disambiguation2/5

ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, creating a true duplicate. The six-tool Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) plus discover_tools vs suggest_questions give agents overlapping entry points that require deep reading to disambiguate.

Naming Consistency3/5

Sub-families are internally consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the server mixes verb_noun, domain_noun, and bare-verb styles across tools. bet_research breaks the polymarket_ prefix pattern, and ai_visibility_check vs scan_competitor_ai_presence use different words for the same underlying concept.

Tool Count2/5

33 tools is heavy and spans at least six unrelated domains: a data-gateway, prediction markets, key-value memory, subscription management, PRIDE proteomics, and standalone utilities (generate_llms_txt, scan_dependency). The scope is so broad that it feels like multiple servers merged into one, making the surface hard to navigate.

Completeness4/5

The dominant data-query domain is well covered: query, grounded query, deep research, profiles, comparison, change feeds, validation, entity resolution, and discovery are all present. Subscription and memory lifecycles are complete, and the prediction-market research surface is thorough; minor gaps exist only in peripheral areas like PRIDE project download/file details.

Resources