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Glama

Harrypotter

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?

Beyond the annotations' readOnly/openWorld/idempotent hints, the description discloses the fan-out strategy across SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, news, and GLEIF, plus the sources_used/sources_failed contract. It also calls out the USPTO API sunset soft-fail, expected empty fda_products for non-biologic companies, and resolved:false for private companies—substantial behavioral context not visible in the structured fields.

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 dense, and it is front-loaded with the most important usage and routing guidance before working through per-source output semantics. There is some redundancy with the schema's value description and a minor 'person/place coming soon' aside, but overall the length is justified by the tool's complexity and the absence of an output schema.

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 this tool has no output schema and fans out across eight data sources, the description compensates thoroughly by listing return fields, failure semantics, value resolution behavior, and expected empty results. An agent has enough context to invoke it correctly, interpret sparse results, and know when a returned empty section is legitimate 'no data' rather than an error.

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 the baseline is 3. The description adds value by giving concrete examples for tickers, zero-padded CIKs, and company names, clarifying that 'company' and 'ticker' are interchangeable, and explaining that a private company yields resolved:false with an explicit notes line rather than a bare failure.

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 opens with concrete user intents ('Tell me about X' / 'research Acme') and states a specific verb/resource: 'full cross-source profile of a US public company in ONE parallel call.' It enumerates the sources and return fields, and explicitly distinguishes itself from chaining single-pack SEC/XBRL/news lookups, making its purpose unmistakable.

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?

The description gives an explicit directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' and provides trigger phrases. It also clarifies edge cases like private companies returning resolved:false and empty sections being real no-data, but it doesn't name sibling alternatives such as compare_entities or resolve_entity.

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

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in data-query functionality. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) also have significant conceptual overlap.

Naming Consistency3/5

All names use snake_case, so there's no camelCase mixing, but conventions vary widely: some are verb_noun (list_characters, resolve_entity, validate_claim), some are noun phrases (entity_profile, recent_alerts, polymarket_edges), and some use a product prefix (ask_pipeworx, pipeworx_feedback). The Harry Potter subset (list_characters, list_spells, list_staff, list_students) is consistent, but the overall set lacks a unified pattern.

Tool Count2/5

35 tools is well above the 25+ threshold that feels heavy, and the server's stated name suggests a narrow Harry Potter scope, yet the vast majority of tools are unrelated Pipeworx data utilities. The count appears bloated and misaligned with the apparent purpose.

Completeness2/5

For a Harry Potter server, the four listing tools are thin (no detail lookups, no filtering by ID, no sort or random), leaving obvious gaps. For a Pipeworx data server, the set is broad but still lacks obvious additions like a generic list-sources tool. The mismatch between name and content makes it impossible to call the surface complete for any single purpose.

Resources