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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

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, indicating safe, read-only behavior. The description adds significant behavioral context: it 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.' It also explains alias handling. 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.

Conciseness5/5

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

The description is two sentences: the first clearly states purpose and usage, the second explains the return value and strategic use. Every sentence is necessary and efficiently packed with information. No redundancy or fluff.

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 that the schema covers all parameters (100%) and no output schema is present, the description fully compensates by explaining exactly what is returned (names, descriptions, full schemas with examples) and that results are directly callable. It also mentions the default and max limit. For a tool of moderate complexity, this is complete.

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% with 6 parameters, many of which are aliases for 'query'. The description adds meaning by explaining that 'query' accepts natural language and that aliases like 'task', 'q', 'description', and 'search' are accepted. This clarifies usage beyond the schema, which only lists them as type string. However, the schema already provides descriptions for each alias. The additional value is moderate, so a 4 is appropriate.

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 (SEC filings, financials, etc.) and explicitly distinguishes it from sibling tools by stating 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This makes the purpose unambiguous 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 provides explicit usage guidance: '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.' It implies when-not-to-use by contrasting with tools that provide just one answer. No alternatives are named, but the context is clear enough.

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

A3.5/5.0
Disambiguation2/5

Several tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve 'find/query Pipeworx data' with blurry boundaries, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) have overlapping purposes. An agent could easily pick the wrong one without reading every description.

Naming Consistency3/5

Most tools follow snake_case verb_noun patterns (list_dataflows, get_data, compare_entities, resolve_entity), and families share prefixes (pipeworx_*, polymarket_*, ask_pipeworx_*). However, the server is named 'Unicef' while almost all tool names reference Pipeworx/Polymarket, and verb choices vary widely, so the overall set lacks a unified naming story.

Tool Count1/5

34 tools is already on the high side, but the real problem is scope: only 3 tools (list_dataflows, dataflow_structure, get_data) relate to the server's stated UNICEF purpose, while the other 31 are an unrelated grab bag of Pipeworx research, prediction-market betting, memory utilities, npm scanning, and llms.txt generation. This is a severe mismatch between count and purpose.

Completeness2/5

For the UNICEF domain implied by the server name, the surface is minimal: browse, structure, and fetch data cover read-only access but nothing else, and the overwhelming majority of tools are off-domain. If the inferred domain is instead 'Pipeworx + prediction markets', coverage is broad, but then the server name is fundamentally misleading and the UNICEF subset is an incomplete afterthought.