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flood

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?

Even with readOnlyHint, openWorldHint, idempotentHint, and destructiveHint annotations already present, the description adds substantial behavioral detail: it explains that enrichment degrades gracefully, that ambiguous instrument matches return figi_candidates rather than asserting, that unresolved identifiers appear under `unresolved`, and that each call internally cascades through several lookup endpoints. This goes well beyond what the annotations declare.

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 query examples and every sentence carries useful information about behavior, supported types, or edge-case handling. It is not maximally concise, yet it avoids filler and organizes the content clearly enough that an agent can extract the key guidance.

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 there is no output schema, the description carries the full burden of describing what the tool returns. It explains the identifier types returned, source labels, the unresolved-field convention, the figi_candidates ambiguity behavior, and graceful degradation when enrichment services are unavailable. Nothing essential for correct invocation or result interpretation appears to be missing.

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?

Although the input schema already covers both parameters at 100%, the description adds crucial semantic detail, especially for the `value` parameter: pass the ENTITY NAME ONLY, for a bond that means the issuer exactly as printed, never the full noun phrase. It also explains what the `type` enum implies for the resolution path, which materially helps an agent invoke the tool correctly.

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 a user-spoken name to canonical/official identifiers that other tools require as input. It immediately distinguishes this tool from siblings by positioning it as the identifier-lookup step, with concrete example queries and supported types. The 'Use FIRST whenever you have a name but need an ID' line makes its role unmistakable.

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?

The description gives explicit usage guidance: 'Use FIRST whenever you have a name but need an ID,' and it clarifies that a call to resolve_entity replaces 2-3 manual lookups. It does not explicitly name alternative sibling tools or state when not to use it, but the trigger conditions and scope are clearly conveyed through examples and supported types.

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 have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all analyze prediction-market opportunities; discover_tools and suggest_questions both serve as meta-tool onboarding. An agent could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but naming patterns are mixed: some are verb_noun (get_flood_forecast, list_subscriptions), some are noun-centric (entity_profile, bet_research), some are bare verbs (remember, recall, forget), and some use long descriptive phrases (ask_pipeworx_grounded, polymarket_kalshi_spread). It is readable but lacks a single predictable convention.

Tool Count2/5

33 tools is heavy for a server whose apparent core is flood forecasting — only 2 of 33 tools (get_flood_forecast, get_river_discharge) relate to flooding. The bulk is a sprawling Pipeworx data-access, prediction-market, and memory layer, making the surface feel overstuffed and off-topic relative to the server name.

Completeness2/5

If the intended domain is flood data, the surface is severely incomplete: only forecast and discharge lookups exist, with no historical flood events, alert subscriptions, mapping, or severity-warning tools. If instead the domain is meant to be Pipeworx-style data research, the surface is broad but still has gaps (no direct SEC filing text retrieval, no clear update/delete lifecycle for many resources). Either way, the purpose is unclear and coverage is mismatched.

Resources