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Glama

Epss

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), description reveals multi-source fan-out, fallback mechanism, PatentsView API sunset issue, and return format (citation URIs). 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?

Single paragraph is dense with information but could be more scannable (e.g., bullet points). No wasted sentences; every sentence adds value.

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?

No output schema, but description sufficiently describes return structure (changes[], total_changes, citation URIs). Covers parameters, behavior, and alternatives, making it complete for an agent to invoke correctly.

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 already describes all 3 parameters (100% coverage). Description adds value with examples of ISO dates and relative shorthands, and explains value accepts ticker or CIK.

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?

Description uses specific verbs ('returns structured changes[]'), names the resources (SEC EDGAR, GDELT, GNews, USPTO), and distinguishes from sibling 'entity_profile' by contrasting use cases ('what's new' vs 'static profile').

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 tells when to use entity_profile instead, and provides context on fallback behavior (GDELT preferred, GNews on rate limit/5xx). Could add more explicit 'do not use when' scenarios, but the alternative is clearly stated.

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