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Glama

Nasa

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, news, GLEIF and returns: cik + company_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); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today; person/place coming soon.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds details: fans out across multiple sources, returns specific fields, notes soft-fail for patents (USPTO API sunset). No contradictions.

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?

Description is somewhat long but well-structured. Front-loaded with examples and key guidance. Each sentence adds value, though could be slightly more concise.

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?

No output schema, but description thoroughly explains return fields (cik, recent_filings, fundamentals, patents, news, LEI) and limitations (names unsupported, patents soft-fail). Complete for a complex multi-source tool.

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 100%, baseline 3. Description adds beyond schema: clarifies value must be ticker or CIK (not names), gives examples, and notes type is only 'company' for now.

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 provides a full cross-source profile of a US public company, with multiple example queries. It distinguishes itself from siblings by advising preference over chaining single-pack lookups.

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?

Explicitly states when to use (holistic view) and when not (if only a name, use resolve_entity first). Also says 'ALWAYS PREFER' over alternative approaches.

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
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded are three variants of the same router, while bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. The descriptions are detailed, but an agent must read extensively to avoid selecting the wrong tool within each cluster.

Naming Consistency2/5

Naming is a mix of conventions: get_*/search_* for NASA tools, ask_pipeworx_* and polymarket_* family prefixes, plus one-off names like entity_profile, bet_research, deep_research, recent_changes, and scan_dependency. There is no consistent verb_noun or family-wide pattern, making tool selection unpredictable despite each individual name being readable.

Tool Count2/5

36 tools is heavy for a server named Nasa, and only 5 of them are actually NASA-related; the rest form a sprawling general data-research, prediction-market, memory, and subscription toolkit. The count is borderline defensible for a broad data assistant, but it is clearly unjustified under the server's stated NASA identity.

Completeness3/5

As a general data-research assistant the surface is quite complete: discovery, routing, grounded verification, entity profiles, comparisons, memory, subscriptions, and feedback are all covered. As a NASA server, however, there are notable gaps—no EONET events, Earth observation, exoplanet archive, or TLE/mission-specific data—and the large non-NASA tool surface does not fill those gaps.

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