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Glama

Pub Dev

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

A4.4/5.0
Behavior5/5

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

Annotations indicate readonly, idempotent, non-destructive. The description adds that results include full schemas with examples and are ready to call directly, which provides behavioral 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.

Conciseness4/5

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

The description is well-structured and front-loaded with purpose, but includes a long list of domains that could be condensed. Most sentences earn their place.

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?

Given the tool's simplicity (1 required param, 100% schema coverage), the description fully explains expected behavior and return value (top-N tools with schemas), making it easy for an agent to use.

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 100% with parameter descriptions. The description adds no new parameter details beyond what the schema already provides, and thus adds minimal extra value.

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 lists specific domains (SEC filings, financials, etc.). It distinguishes from siblings by noting 'Call this FIRST when you have many tools available and want to see the option set.'

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?

The description explicitly states when to use ('Use when you need to browse, search...') and advises calling it FIRST. However, it does not explicitly state when not to use it, such as when you already know the 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

A4/5.0
Disambiguation3/5

Several tools have clear functional boundaries, but there is meaningful overlap at the top level: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share one routing pipeline, and ask_pipeworx_beta is currently identical to ask_pipeworx. The six polymarket_* tools also form a dense family where an agent must read long descriptions to distinguish arbitrage scanning from edge detection from fill-risk evaluation.

Naming Consistency4/5

Names are uniformly lowercase snake_case and mostly follow verb_noun or domain-prefix conventions, which makes the set much more predictable than its count suggests. Minor deviations exist: ask_pipeworx variants are product-noun phrases, polymarket_edges is a noun phrase rather than a verb-led tool, and pairs like polymarket_edges vs polymarket_edge_tracker are easy to misread.

Tool Count2/5

With 31 tools, this server is above the 25+ threshold and feels overloaded for a single MCP surface. It mixes broad data research, prediction-market analysis, memory, subscriptions, feedback, and niche utilities like generate_llms_txt, so the set is more like several related servers merged together.

Completeness4/5

The core workflow is well covered: discovery, single-answer routing, grounded verification, deep research, entity resolution, comparison, change tracking, subscription lifecycle, and even memory primitives. Missing are minor lifecycle refinements such as updating an existing subscription, and the number of overlapping entry points makes it slightly harder to guarantee the agent will always choose the intended path.

Resources