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imgflip

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already signal read-only, open-world, idempotent, non-destructive behavior. The description goes well beyond that by disclosing source-specific quirks: USPTO patent API sunset with soft-fail, expected empty fda_products for non-biologic companies, sources_used/sources_failed semantics, and resolved:false for private companies. No contradiction exists.

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 deliberately dense, with useful intent examples front-loaded before output details and edge cases. It is not wasteful, though the enumerate-then-explain structure makes it somewhat heavy to parse; a bit of structural tightening could improve scannability.

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 the tool's complexity, multiple data sources, and lack of an output schema, the description is impressively complete. It covers return shape, source failure semantics, expected empty results, private-company behavior, and input normalization, so an agent has enough context to call and interpret this tool correctly.

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

Parameters3/5

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

Schema coverage is 100% and the schema itself already documents that type and value accept ticker, CIK, or company name interchangeably. While the description reinforces this with examples and the zero-padded CIK convention, it adds little meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 is precise: it profiles a US public company by fanning out across many sources and returning a comprehensive entity profile in one call. The examples and 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' clearly distinguish this tool from narrower one-off lookups and sibling research tools.

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?

Explicit guidance is given: prefer this tool for holistic company research instead of chaining multiple single-pack calls. It also specifies accepted inputs (ticker, CIK, name), the private-company edge case, and expected empty sections, leaving little ambiguity about when and how to invoke it.

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 have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all target prediction-market analysis. Long descriptions clarify some boundaries, but an agent could easily misroute a query to the wrong variant.

Naming Consistency3/5

Names are mostly snake_case and readable, with consistent families like ask_pipeworx_* and polymarket_*, but conventions vary widely: verb-first (validate_claim, compare_entities, generate_llms_txt), noun-first (entity_profile, bet_research, recent_changes), and standalone verbs (remember, recall, forget). The pipeworx_ prefix is applied inconsistently, and get_memes sits apart from the data-tool naming style.

Tool Count2/5

32 tools exceeds the comfortable range and the count is inflated by near-duplicate variants (three ask_pipeworx versions, six Polymarket tools). The scope mismatch compounds the issue: the server is named imgflip but nearly all tools belong to Pipeworx data/prediction-market functionality, so the set feels bloated rather than deliberately scoped.

Completeness1/5

As an imgflip/meme server the surface is severely incomplete: get_memes explicitly refers to caption_image for creating memes, but that tool is absent, creating a dead end. The remaining tools cover a broad but unrelated Pipeworx data domain, so no coherent domain gets full lifecycle coverage and the tool set fails its apparent core purpose.

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