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Glama

Ebird

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?

The description goes far beyond the readOnly/idempotent annotations by disclosing multi-source fan-out, soft-failure of USPTO patents, expected empty fda_products for small-molecule-only companies, resolved:false for private companies, and sources_used/sources_failed semantics. It also explains edge cases like name resolution and interchangeability of type values, giving the agent a realistic model of tool behavior.

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 it is front-loaded with user-intent examples and a one-sentence summary, then organized as a dense list of return sections and edge cases. Nearly every clause provides distinct information, though some parameter details are redundantly repeated from the 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?

With no output schema, the description carries the full burden of explaining return structure and failure modes. It covers all data sections, source fallback, edge cases (private company, empty sections, patents sunset), and return field semantics. An agent has enough context to invoke the tool correctly and interpret expected outcomes.

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 schema already documents both parameters. The description adds meaningful extra context: value can be a ticker, zero-padded CIK, or company name; names resolve via SEC EDGAR; type values are interchangeable; and private companies with no CIK return resolved:false. This goes beyond the schema's basic 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?

The description states a specific verb and resource: it builds a 'full cross-source profile of a US public company in ONE parallel call,' and gives multiple natural-language examples like 'Tell me about X' and 'brief me on Tesla.' It clearly distinguishes itself from chained single-pack lookups and sibling tools by emphasizing the holistic, parallel nature of its output.

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?

Usage guidance is explicit: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' and it lists trigger phrases. It does not explicitly advise against using sibling tools like compare_entities or resolve_entity, but the condition and alternative are clearly stated.

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

A3.6/5.0
Disambiguation3/5

Several tools are close cousins: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying data catalog, and bet_research/polymarket_edges/polymarket_arbitrage share a betting-research niche. The eBird tools are clearly a different cluster, but the server carries so many unrelated domains that an agent may struggle to pick the right category member (e.g., stable router vs beta router vs grounded router).

Naming Consistency3/5

Almost everything is snake_case, but the pattern is not uniform: there are plenty of verb_noun names (find_species, list_subregions, scan_competitor_ai_presence) mixed with bare consumer-style names (ask_pipeworx, bet_research, entity_profile, deep_research) and short helpers (recall, forget, remember). No mixed scripting-case chaos, but no consistent verb_noun or noun_verb system either.

Tool Count2/5

36 tools is heavy for a server that calls itself Ebird: only ~5 tools actually relate to bird observation, while the rest span Pipeworx data lookups, Polymarket betting, legal/regulatory analyzers, memory, subscriptions, competency scanning, npm package checking, and llms.txt generation. The count would be reasonable for a broad data platform, but the server's stated identity and the bundled tool set do not match, making the scope feel bloated and incoherent.

Completeness2/5

For a bird-centric server, the surface is thin: you can find a species, list subregions, and pull recent/notable observations, but you cannot get coordinated eBird atlases, hotspot details, species life-history stats, or region-based species lists. For the larger set of unrelated tools, completeness is impossible to gauge about a missing domain; the eBird purpose feels unfinished even though the miscellaneous tools are overloaded.

Resources