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Glama

Ulid

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

A4.1/5.0
Behavior4/5

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

Annotations provide readOnlyHint and idempotentHint. Description adds behavior: 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?

Two efficient sentences plus a list. Front-loaded with purpose. Could tighten domain list but overall well-structured.

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 description fully specifies return value: tool names, descriptions, schemas. Sufficient for a discovery tool.

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%, so baseline is 3. Description mentions aliases and natural language usage but adds little beyond schema examples.

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?

Explicitly states 'Find tools by describing the data or task' and lists specific domains. Clearly distinguishes from siblings by being the discovery tool for all other 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?

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set' — clear context but no explicit when-not-to-use guidance.

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

A3.8/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query entry points (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now); polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research form a dense prediction-market suite; ai_visibility_check and scan_competitor_ai_presence are single vs. multi variants. An agent would frequently struggle to pick the right tool without reading long descriptions.

Naming Consistency4/5

Most tools follow a clean snake_case convention and are mostly verb_noun (generate_ulid, parse_ulid, list_subscriptions, resolve_entity, validate_claim, compare_entities), making the set predictable. Minor deviations exist (entity_profile, deep_research, bet_research, ask_pipeworx_beta) but they are still readable and don't break the overall pattern.

Tool Count2/5

33 tools is well above the 25-tool threshold for a heavy surface, and the server name 'Ulid' suggests a tiny scope that wildly mismatches the actual content. While the real domain (Pipeworx data + prediction markets) is broad, the set bundles many subdomains into one server, making navigation and selection costly.

Completeness4/5

For the actual apparent domain—structured data research, entity lookups, verification, prediction-market analysis, memory, and subscriptions—coverage is strong: query, grounded query, deep research, entity profiles, comparisons, resolution, validation, discovery, alerts, and memory tools are all present. ULID functionality is minimal but sufficient (generate + parse). Minor gaps exist (e.g., no direct single-source browser beyond discover_tools) but agents can work around them.

Resources