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Glama

Epss

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint. The description adds context: results are 'top-N most relevant', include 'curated examples', and are '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?

The description is front-loaded with purpose and usage, though slightly lengthy due to listing domains. Every sentence adds value, but could be trimmed slightly.

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?

The description fully explains what the tool does, when to use it, what it returns, and provides examples. No output schema exists, but the description compensates by describing the output nature.

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 description coverage is 100%, so the schema already defines parameters well. The description provides example queries and mentions aliases, but adds limited extra meaning beyond what the schema offers.

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 discovers tools by describing a data or task, and explicitly lists many domains. It distinguishes itself from siblings by emphasizing it returns ready-to-call tools with full schemas.

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 'Call this FIRST when you have many tools available and want to see the option set'. It also provides a list of example queries, guiding the agent on when to use this 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

A3.7/5.0
Disambiguation2/5

Several tools occupy overlapping functional space: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and bet_research, polymarket_edges, and polymarket_arbitrage all target Polymarket opportunity detection. The detailed descriptions help, but an agent can easily select the wrong query/research or prediction-market tool.

Naming Consistency3/5

Most names are readable snake_case and clusters like polymarket_* and pipeworx_* are internally consistent. However, the overall set mixes verb_object names (compare_entities, resolve_entity), bare verbs (forget, subscribe), and noun phrases (entity_profile, recent_alerts, top_exploited), so there is no unifying naming convention.

Tool Count2/5

At 33 tools, the count exceeds the reasonable threshold for a focused server, and the problem is worse because the server is named Epss while most tools are Pipeworx data, Polymarket, memory, and subscription tools. A focused EPSS server would need only a handful of tools; this is a grab bag.

Completeness2/5

For the EPSS purpose implied by the server name, only get_epss and top_exploited exist, with no CVE search, historical score context, or vulnerability-management tooling. The unrelated research, memory, and prediction-market tools are individually fairly complete, but they do not fill the gap for the apparent EPSS use case.

Resources