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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

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: the tool returns schemas with curated examples ready to call, and accepts multiple aliases for 'query.' 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 concise (5 sentences) with front-loaded purpose, usage guidance, and return format. Every sentence adds value, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but the description explains return format (names, descriptions, schemas with examples). It covers key aspects for a discovery tool, though could mention if relevance scores are returned.

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%, baseline 3. The description adds meaning by explaining 'query' as a natural language description, providing examples, and stating aliases like 'task' and 'search.' This goes beyond schema definitions.

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 defines the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains and states it returns 'top-N most relevant tools with names, descriptions, and full input schemas.' This distinguishes it from sibling tools that perform other tasks.

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 advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It provides clear context for when to use, though it does not explicitly state when not to use or name alternatives.

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

A3.7/5.0
Disambiguation2/5

The tool set contains multiple clusters with heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying data sources, making it ambiguous which to choose. Similarly, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and bet_research all target prediction-market opportunities with fuzzy boundaries between them. The three DigitalNZ tools (search, record, search_within) are distinct, but the rest of the set obscures their purpose.

Naming Consistency3/5

Most tools use snake_case with descriptive names, and the polymarket_* cluster is consistent among itself. However, conventions are mixed: some are verb-first (validate_claim, discover_tools, remember), some are noun-first (entity_profile, recent_changes), and ask_pipeworx_beta breaks the pattern with a suffix variant. The naming is readable overall but not uniform.

Tool Count2/5

At 33 tools, the server exceeds the 25-tool threshold for a 'too heavy' count. The vast majority of tools belong to the Pipeworx data-query and prediction-market domains rather than DigitalNZ, which is the server's stated name. A focused DigitalNZ server would need closer to 5-10 tools; a Pipeworx server would still be over-packed at 33 given the functional overlap.

Completeness2/5

For the DigitalNZ domain, the surface is severely incomplete: only search, record, and search_within exist, with no browse, filter, facet, or contribution capabilities. The Pipeworx side is more complete but still has gaps (e.g., no direct per-source query tools, and several tools soft-fail on sunset APIs). The server tries to cover two unrelated domains and satisfies neither fully.