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Glama

Gsa Perdiem

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

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, not destructive), the description adds key behavioral traits: fan-out to multiple sources, fallback logic, and USPTO sunset limitation. It also describes the return structure (grouped changes, total count, citation URIs), which annotations lack.

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 detailed yet concise, front-loaded with example queries. Every sentence serves a purpose: stating function, listing sources, explaining fallback, defining parameter semantics, and distinguishing from siblings. No wasted words.

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?

Given the tool's complexity (multiple data sources, fallback, output without schema), the description covers all essential aspects: input examples, source behavior, return structure, and limitations. It fully equips an agent to use the tool 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 coverage is 100%, but the description adds valuable examples and usage hints (e.g., ISO date format, relative shorthand like '30d', ticker vs. CIK). It clarifies that 'type' only supports 'company' and provides typical values for 'since'. This exceeds baseline 3 by offering practical guidance.

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's a change feed for a company in a time window, listing example queries and specific data sources (SEC, GDELT/GNews, USPTO). It explicitly distinguishes from the sibling tool 'entity_profile' for static profiles, ensuring no confusion.

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 provides explicit when-to-use guidance (e.g., 'What's new with X') and directs users to 'entity_profile' for static profiles. It also details fallback behavior (GDELT→GNews) and notes USPTO soft-fail, giving clear context for usage.

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 have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer broad data questions with only subtle differences, and the Polymarket suite (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) has blurry boundaries. An agent could easily misselect among these without reading the full descriptions.

Naming Consistency3/5

There are recognizable prefixes like polymarket_*, rates_by_*, and ask_pipeworx_*, but the set mixes verb_thing, thing_verb, and bare-noun names (deep_research, entity_profile, validate_claim, remember, generate_llms_txt, suggest_questions). The conventions are readable but not uniform across the full surface.

Tool Count2/5

33 tools is above the threshold for a focused server, and the server name 'Gsa Perdiem' suggests a tiny domain that only two of the tools (rates_by_city, rates_by_zip) actually serve. Even for the broad Pipeworx platform scope, the many near-duplicate meta-tools and overlapping prediction-market tools make the count feel bloated.

Completeness3/5

The broad data-research surface is fairly complete: entity resolution, profiling, changes, grounded Q&A, memory CRUD, subscriptions, and prediction-market analysis all have coverage. However, the per-diem domain implied by the server name is under-served (no state-level lookup, no fiscal-year selection), and the mix of two domain-specific tools with a sprawling platform creates obvious gaps relative to the stated identity.

Resources