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Slack_connect

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 declare readOnly/openWorld/idempotent, and the description adds substantial behavioral depth beyond that: internal cascading through multiple lookup endpoints, graceful degradation when GLEIF or OpenFIGI is unavailable, behavior on ambiguous matches (returns `figi_candidates` and asserts nothing), explicit marking of unresolved identifiers under `unresolved`, and source labeling for every identifier. This gives the agent accurate expectations about edge cases and failure modes.

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 nearly every sentence earns its place given the tool's complexity (two entity types, multiple identifier systems, enrichment fallbacks). It is front-loaded with examples and the central 'Use FIRST' directive. A slight redundancy exists in repeating the cascade point and some over-explanation, but overall it remains well-organized and focused.

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?

The tool is complex—two types, multiple identifier sources, no output schema—yet the description covers input validation caveats, ambiguous-match behavior, unresolved-identifier reporting, and external-service degradation. An agent has everything needed to call it correctly and interpret results, especially given the rich annotations.

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%, but the description adds high-value meaning beyond the schema. For `value`, it warns to pass the entity name only, explains that trailing security-class words will match nothing in FIGI lookups, and gives a concrete bond-issuer example. For `type`, it details resolution behavior for each supported type. This is far beyond a baseline repetition of the schema.

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 queries and states a specific action: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly distinguishes from siblings like entity_profile and compare_entities by positioning itself as the name-to-ID lookup, and even instructs 'Use FIRST whenever you have a name but need an ID.'

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?

Explicit usage guidance is front-loaded: 'Use FIRST whenever you have a name but need an ID,' reinforced by query examples across supported types. It clearly explains what input variants are accepted (ticker, CIK, ISIN, brand/generic name). It does not explicitly name alternative tools to use when an ID is already known, so it lacks a full when-not-to-use exclusion, but the context is otherwise clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Slack_connect toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

The five Slack tools are distinct, but the rest of the set is a sprawling bundle of Pipeworx, prediction-market, memory, and subscription tools with several overlapping pairs: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, while discover_tools and suggest_questions both act as discovery entry points and scan_competitor_ai_presence wraps ai_visibility_check. An agent would frequently have to read long caveats to choose the right tool.

Naming Consistency3/5

Most names are descriptive snake_case and the Slack tools share a clean slack_ prefix, but the broader set mixes verb-led names (ask_pipeworx, generate_llms_txt, validate_claim) with noun-style names (entity_profile, pipeworx_trending, ai_visibility_check) and a few bare verbs (remember, recall, forget, subscribe). It is readable but does not follow a single predictable pattern.

Tool Count2/5

36 tools is above the 25+ threshold and far more than a Slack connector needs: only five tools actually interact with Slack, while the other 31 are unrelated Pipeworx research, prediction-market, memory, and subscription features. The set reads as a kitchen-sink bundle rather than a focused integration.

Completeness2/5

For a Slack_connect server, the surface is only partially complete: it can list channels/users, join, read history, and send messages, but common Slack operations like threads, reactions, message update/delete, channel creation/archiving, and direct messages are missing. The unrelated data tools do not fill these gaps, so the actual Slack domain would still cause agent failures.

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