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dns

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already signal read-only/idempotent safety, but the description goes well beyond that: identifiers are labeled by source, unresolved identifiers are returned under `unresolved`, ambiguous FIGI matches return candidates instead of asserting, LEI/FIGI enrichment degrades gracefully, and ISINs resolve to the issuing legal entity via GLEIF. No contradiction with annotations.

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 every section earns its place: purpose, supported types, edge cases, degradation behavior, and practical value. It is structured rather than rambling, though it could be marginally tighter and more paragraph-separated given its density.

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?

There is no output schema, so the description carries the full burden of explaining return behavior. It explicitly covers what is returned for company and drug types, how unresolved identifiers are handled, what happens with ambiguous names, and the graceful degradation path. An agent has enough to call and interpret the result correctly.

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

Parameters5/5

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 substantial meaning beyond the schema by detailing the company identity spine (CIK/ticker/company_name/LEI/FIGI), source attribution, ISIN-to-LEI behavior, ambiguity handling, and the drug return fields (RxCUI, ingredient, brand, citation). This materially helps an agent pass the right value.

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 resolves user-spoken names to canonical/official identifiers that other tools require. The example queries ('ticker for', 'CIK', 'LEI', 'RxCUI') make its scope unmistakable, and the explicit 'Use FIRST whenever you have a name but need an ID' distinguishes it from siblings like entity_profile or compare_entities.

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?

It gives a very clear trigger: 'Use FIRST whenever you have a name but need an ID', plus enumerated supported types and query patterns. It even explains what to do on ambiguous matches. However, it does not explicitly name alternatives or give when-not-to-use conditions, so it stops short of a full 5.

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

A3.8/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly described as currently identical, creating real ambiguity between two tools. The polymarket cluster (arbitrage, edges, fill_risk, edge_tracker, kalshi_spread, bet_research) has overlapping edge-finding purposes that rely on reading long descriptions to separate, and the DNS tools are so few that an agent cannot tell this is a 'dns' server at all.

Naming Consistency2/5

The set mixes at least four naming conventions: bare verbs (remember, forget, subscribe), verb_noun (dns_lookup, validate_claim, discover_tools), noun phrases (entity_profile, deep_research, recent_alerts), and brand-prefixed families (ask_pipeworx_*, polymarket_*, pipeworx_*). Each cluster is internally consistent, but the overall pattern is incoherent, including stray names like reverse_dns that invert the verb_first convention.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold for a heavy surface, and the count is wildly mismatched to the server's name: only 3 of 34 tools (dns_lookup, dns_lookup_all, reverse_dns) relate to DNS. The remaining 31 tools belong to unrelated domains (data research, prediction markets, memory, subscriptions, npm scanning), making the toolkit feel like a mislabeled grab-bag rather than a scoped server.

Completeness3/5

Judged against the server's stated 'dns' purpose, coverage is thin: read-only lookups only, with no WHOIS, DNSSEC, zone management, or write operations. Judged against the dominant inferred domain (a data-research/prediction-market platform), the surface is quite complete — query, grounded verification, deep research, profiles, comparisons, claim validation, discovery, subscriptions, alerts, and feedback all exist — though there is no tool to directly read a pipeworx:// citation URI.

Resources