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Glama

Vat

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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. Description adds value by stating results are ready to call directly and include schemas, but does not disclose pagination or ordering details.

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?

Description is a single, well-structured paragraph that front-loads the core purpose, then provides usage context, output details, and a call to action. Every sentence adds value with 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?

Despite no output schema, the description adequately explains the return value (tools with names, descriptions, and schemas). It doesn't elaborate on ranking or limits, but given simplicity and annotations, it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant context beyond the schema: explains 'query' as a natural language description with examples, mentions aliases, and gives concrete use cases. This helps the agent craft effective queries.

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 it finds tools by describing data or task, and distinguishes from sibling tools by being a discovery/ browsing tool. It lists specific domains and explains the output format, ensuring no ambiguity.

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?

Explicitly instructs 'Call this FIRST when you have many tools available and want to see the option set', providing clear when-to-use guidance. Also gives example queries and mentions alternatives are not needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Vat toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying catalog, and ask_pipeworx_beta is currently identical to ask_pipeworx. ai_visibility_check and scan_competitor_ai_presence also overlap, and entity_profile vs recent_changes cover similar ground. The descriptions are detailed, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (validate_vat, list_vat_formats, resolve_entity), some are noun-first (entity_profile, polymarket_edges, recent_alerts), and some are product-branded (ask_pipeworx, pipeworx_feedback). It's readable but does not follow a single predictable verb_noun convention.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent set, especially for a server named 'Vat' where only two tools (list_vat_formats, validate_vat) relate to the apparent purpose. The bulk forms a broad data-research platform that would be more appropriately split into separate VAT and Pipeworx servers.

Completeness4/5

For the actual data-research domain, coverage is strong: discovery, single lookup, grounded answers, deep multi-source research, entity resolution, comparisons, claim verification, memory, subscriptions, and prediction-market tooling are all present. The only clear gap is that the VAT-specific surface is minimal (format validation only, no registration/VIES check), but the overall functional surface is otherwise quite complete.

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