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Glama

Ashby

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: fan-out behavior, fallback mechanism from GDELT to GNews on rate limits/5xx, USPTO soft-failure due to PatentsView API sunset, and the returned structure (changes grouped by source, total_changes count, pipeworx:// URIs). No contradiction.

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 a single dense paragraph that efficiently conveys all needed information. It is front-loaded with example queries. Could be slightly more structured (e.g., bullet points for sources) but remains concise and informative.

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?

Despite no output schema, the description adequately explains the return format and all key behavioral aspects: multi-source fan-out, fallback logic, known limitations (USPTO soft-fail), and parameter usage. Covers all essential context for 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%, so baseline is 3. The description adds extra meaning: relative shorthand for 'since' (e.g., '7d', '1y'), explicit recommendation of '30d' or '1m' for monitoring, and examples of valid 'value' inputs (ticker or CIK). This exceeds the baseline.

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 the tool's purpose as a change feed for a company within a time window, listing specific data sources (SEC EDGAR, GDELT→GNews, USPTO) and explicitly distinguishes itself from the sibling tool 'entity_profile' by contrasting dynamic vs static profiles.

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 usage guidance, including example queries ('What's new with X', 'latest on Y'), when to use this tool vs 'entity_profile', and details on the 'since' parameter format with recommended defaults ('30d' or '1m').

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

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants; polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all target prediction-market opportunities. Research tools like entity_profile, compare_entities, recent_changes, and validate_claim also blur together, making it hard to pick the right tool.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use verb_noun (get_candidate, list_applications, remember), others use noun_verb (bet_research, entity_profile) or prefix-only patterns (ask_pipeworx, pipeworx_feedback, polymarket_edges). The Ashby tools use ashby_ prefix, but the rest mix pipeworx_, polymarket_, and bare names, with no uniform style.

Tool Count2/5

36 tools is excessive for a coherent server and spans unrelated domains: ATS (Ashby), data queries (Pipeworx), prediction markets (Polymarket), memory, subscriptions, and web utilities. This feels like a kitchen sink rather than a focused toolset, and the count alone makes selection overwhelming.

Completeness2/5

The Ashby ATS subset is incomplete: it provides get/list operations but no create, update, or delete for candidates or jobs, and no interview management. The broader server's scope is so mixed that each domain has obvious gaps, leaving agents unable to complete common workflows end-to-end.

Resources