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Glama

Jsonschema

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

A5/5.0
Behavior5/5

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

The description discloses source-specific behavior (SEC EDGAR, GDELT→GNews fallback on rate-limit/5xx, USPTO soft-fail due to API sunset), date parsing flexibility, and the return structure. This goes well beyond the readOnly/idempotent hints by adding failure modes and external dependencies.

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 dense but perfectly structured: front-loaded examples, then source fan-out, date formats, output summary, and alternative. Every sentence adds distinct value with no repetition or filler.

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?

For a multi-source tool with no output schema, the description covers all necessary ground: input formats, source behaviors, fallback logic, output shape, and an alternative tool. It is practically complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description enriches each parameter: since gets concrete formats ('2026-04-01', '7d', '30d', '3m', '1y'), value is shown as ticker or CIK, and type is confirmed as only 'company'. This adds practical usage context beyond the schema.

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 identifies the tool as a change feed for a company over a recent window, illustrated with specific example queries. It explicitly differentiates from entity_profile by noting the alternative for static profiles, so its purpose is unambiguous and distinct from siblings.

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?

It provides concrete intents ('What's new with X', 'updates on Acme') and explains the fan-out to multiple sources. It explicitly names entity_profile as the alternative for static profiles regardless of window, giving clear when-to-use vs. 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

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same routing and response shape, while the polymarket_* family and company-research tools (entity_profile, compare_entities, recent_changes) have overlapping triggers. The descriptions are detailed, but an agent can still easily select the wrong variant.

Naming Consistency3/5

Names are readable and consistently snake_case, with recognizable families like ask_pipeworx_*, polymarket_*, and pipeworx_*. However, conventions mix verb_noun patterns (compare_entities, resolve_entity) with noun phrases (entity_profile, recent_alerts, pipeworx_trending), so the pattern is not fully consistent.

Tool Count2/5

32 tools is heavy for any single server, and nearly all of them are unrelated to the 'Jsonschema' name—only validate_json_schema actually addresses JSON Schema. Even viewed as a Pipeworx data/research toolkit, the set feels bloated with near-duplicate query and prediction-market variants.

Completeness2/5

If the intended domain is JSON Schema, the surface is severely incomplete: only validation exists, with no parsing, generation, or schema-management tools. If the intended domain is the Pipeworx data-research suite, it is more complete but still lacks a raw record-fetch tool despite citations promising pipeworx:// URIs, and the server-name mismatch creates a confusing dead end.

Resources