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Glama

Jsonschema

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Discloses non-obvious behaviors: USPTO PatentsView API sunset soft-fail, empty sections being real 'no data' rather than errors, private companies returning resolved:false with notes, and `type` accepting 'company' or 'ticker' interchangeably. Annotations already cover read-only/idempotent safety, and the description meaningfully adds beyond-annotation context.

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 long, but every section contributes: examples, core value proposition, source list, output fields, and failure/edge-case semantics. Since there is no output schema, the length is justified and the most important directive ('ALWAYS PREFER...') is front-loaded.

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 only 2 schema-documented parameters and no output schema, the description is exceptionally complete. It covers input resolution, all return sections, source failure modes, empty-section semantics, and private-company behavior. No essential information for correct invocation appears missing.

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 valuable detail: accepted input shapes (ticker, zero-padded CIK, company name), example values, and the note that both `type` values behave identically. This goes beyond the schema's own parameter descriptions.

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 states it returns a 'full cross-source profile of a US public company in ONE parallel call,' enumerating specific sources and output fields. It clearly differentiates itself from chaining single-pack SEC/XBRL/news lookups and from siblings like compare_entities by scoping to a single entity.

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?

Provides concrete example intents ('Tell me about X', 'research Acme') and explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It gives clear when-to-use context, though it does not explicitly contrast with sibling tools like deep_research or compare_entities.

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

Several tools occupy the same entry-point role (ask_pipeworx, ask_pipeworx_beta, deep_research, discover_tools, suggest_questions), and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The Polymarket family is carefully described but still has heavily overlapping discovery/edge surfaces. Only validate_json_schema is unambiguous, and it has no related siblings to clarify its position.

Naming Consistency2/5

Tool names mix verb-first forms (discover_tools, generate_llms_txt, resolve_entity), noun-first forms (entity_profile, pipeworx_feedback), and bare verbs (remember, recall, forget). Prefix conventions are inconsistent—ask_pipeworx, pipeworx_feedback, polymarket_edges, recent_changes—and almost none of the names reflect the server name Jsonschema.

Tool Count1/5

32 tools is already too many, but for a server named Jsonschema only one tool belongs to that domain; the rest form a broad data-research and prediction-market suite. This is an extreme scoping mismatch: the set is simultaneously oversized and almost entirely off-purpose.

Completeness1/5

For a JSON Schema server, the surface is essentially one operation: validate_json_schema. Common schema lifecycle operations—generation, parsing, conversion, ref resolution, linting—are missing, leaving most JSON Schema tasks impossible. The unrelated data-research tools are internally rich, but they do not complete the apparent domain.

Resources