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Glama

Semver

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 annotations already convey read-only, idempotent, and non-destructive behavior. The description adds substantial behavioral context beyond that: parallel fan-out across sources, sources_used/sources_failed semantics, empty sections meaning real absence of data, FDA-related expected-empty behavior, and fallback chains like GDELT->GNews. This meaningfully supplements the structured annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense, run-on paragraph with a lot of critical information packed in. It is front-loaded with examples and user-intent phrasing, but the lack of structural breaks and some redundancy with the schema's type explanation make it harder to scan than it should be for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description bears the burden of explaining return shape. It covers the main result sections, source reporting, failure semantics, and resolution behavior. It is not exhaustive about every field's type or format, but for a two-parameter tool with this much self-describing output, it is sufficiently complete.

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 real semantic value beyond the schema by explaining that 'type' accepts 'company' or 'ticker' interchangeably, that 'value' can be a ticker, zero-padded CIK, or company name, and that name resolution can produce resolved_from/resolved_to and resolved:false outcomes. This goes beyond the schema's field 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 opens with concrete user-phrase examples and states the core purpose: 'full cross-source profile of a US public company in ONE parallel call.' It clearly identifies the resource and the scope, and it contrasts itself with chained single-pack lookups, making it easy for an agent to tell apart from narrow lookup 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?

It gives an explicit usage rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also covers edge-case routing such as private companies returning resolved:false with an explanatory notes line, and notes that person/place support is coming soon, which tells the agent when not to rely on this tool.

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

Several clusters overlap significantly: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve general data-query purposes, and entity_profile, compare_entities, recent_changes, and validate_claim pull from the same SEC/news/data sources in similar ways. The semver utilities are distinct, but they sit alongside unrelated prediction-market, memory, subscription, and AI-visibility tools that make the overall boundary of each tool much fuzzier.

Naming Consistency3/5

Some tools follow a clean verb_noun pattern (parse_semver, compare_semver, compare_entities, resolve_entity), but others are noun phrases (entity_profile, polymarket_edges, recent_changes) or branded/verb-first names (ask_pipeworx, deep_research, bet_research, pipeworx_trending). The mix is readable but inconsistent, with no unifying convention across the 34 tools.

Tool Count2/5

34 tools is too many for a server named Semver, whose actual semver-related surface is only a few utilities. Even viewed as a broad data platform, the count is heavy and padded with unrelated capabilities like prediction-market arbitrage, memory storage, subscriptions, and AI-visibility checks that do not belong together in one server.

Completeness2/5

As a Semver server it covers parse, compare, and range satisfaction but lacks obvious operations like version bumping/incrementing or validating a version list, making the core surface incomplete. As a general data platform the domain is unclear and the unusual mix of semver, market, memory, and marketing tools prevents any coherent completeness assessment.

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