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Glama

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

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral details: returns top-N most relevant tools with names, descriptions, and full input schemas, each result ready to call directly. No contradictions, and it 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.

Conciseness4/5

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

The description is a single paragraph that front-loads purpose and usage. It is efficient but could be slightly more structured. Every sentence adds value, no waste.

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 no output schema, the description fully explains the return format (names, descriptions, full schemas) and the use case. It covers the necessary context for an agent to use the tool effectively.

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 all parameters described. The description does not add significant new meaning for parameters beyond stating that they are aliases and providing examples. The high schema coverage means the description is not required to elaborate, but it does not go above baseline.

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 verb 'Find' and resource 'tools', and distinguishes itself from sibling tools by being a meta-tool for discovery. It explains that it returns relevant tools with full schemas, making its purpose specific and distinct.

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?

The description explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear guidance on when to use and implies when not to (when you already know the 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

Several tools have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, ask_pipeworx_grounded is the same router with an extraction step, and discover_tools vs suggest_questions both serve as 'what can I do here' entry points. The polymarket_* family is more distinct, but the ask_pipeworx/deep_research overlap alone makes tool selection genuinely ambiguous.

Naming Consistency4/5

The vast majority of tools follow lowercase snake_case with recognizable domain prefixes (ask_pipeworx*, polymarket_*, pipeworx_*), which is a decent pattern. However, there are bare-noun tools (events, locations, recall, forget) and mixed verb-first vs noun-first ordering (list_subscriptions vs entity_profile, scan_dependency vs polymarket_edges), so it is not perfectly uniform.

Tool Count2/5

33 tools is well above the comfortable range and feels heavy even for a broad data-research platform. Many tools are narrow variations (five polymarket analysis tools, four ask_pipeworx variants, three memory tools) that could plausibly be consolidated or exposed as parameterized modes rather than separate top-level tools.

Completeness4/5

For the actual domain suggested by the tool names and descriptions—structured data research, entity profiling, claim verification, and prediction-market analysis—the surface is quite complete: lookup, grounded answers, deep research, comparisons, recent-change tracking, subscriptions, memory, and arbitrage/fill-risk analysis are all covered. However, relative to the server name 'Edmtrain', the event-discovery surface is extremely thin (only events and locations), which is a notable mismatch.