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Glama

Crc

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
Behavior4/5

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

Annotations already mark it as readOnly and idempotent. The description adds that it returns top-N tools with full schemas and examples, ready to call directly. No contradictions.

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 key information. It lists many domains, which is slightly redundant but still helpful. No wasted sentences.

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 has 6 parameters, 1 required, full schema coverage, and no output schema, the description is complete. It explains what the tool returns and how to use results, with no gaps.

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 coverage is 100%, so baseline is 3. The description adds value by providing examples of natural language queries and listing aliases for the query parameter, making it more actionable.

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's purpose: 'Find tools by describing the data or task.' It lists numerous domains and explicitly says to call it first to see the option set, distinguishing it from sibling 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?

The description says 'Call this FIRST when you have many tools available and want to see the option set,' providing clear usage context. It does not explicitly state when not to use it, but the context implies it's for discovery, not for direct answers.

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in the prediction market domain (e.g., bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) and data querying (ask_pipeworx, ask_pipeworx_grounded, deep_research). This overlap creates confusion for an agent selecting the right tool.

Naming Consistency2/5

Naming is inconsistent: most tools use snake_case but some start with a verb (ask_, bet_, compare_) while others start with a noun (entity_profile, pipeworx_feedback, polymarket_*). There is no uniform pattern, making it harder to predict tool names.

Tool Count2/5

With 32 tools, the server is overstuffed for a single focus. It bundles checksums, AI visibility, data queries, prediction market analysis, subscriptions, memory, and more, which would be better split into separate, more focused servers.

Completeness2/5

Despite the large tool count, the server lacks completeness in key areas: no tool to place prediction market trades, no direct SEC filing detail extraction (only through generic queries), and only two checksum tools despite the server name 'Crc'. The surface feels scattershot rather than comprehensive.

Resources