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Cfpb

by io.github.pipeworx-io

Cfpb Get Complaint

cfpb_get_complaint
Read-onlyIdempotent

Retrieve full details for a specific complaint by ID. Returns narrative, company response, resolution status, and metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
complaint_idYesCFPB complaint ID number

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
issueYesMain complaint issue
stateNoConsumer state
timelyNoWhether response was timely
companyYesCompany name
productYesProduct category
narrativeNoConsumer complaint narrative
sub_issueNoSubcategory of issue
sub_productNoSubcategory of product
complaint_idYesUnique complaint identifier
date_receivedYesDate complaint was received
submitted_viaNoSubmission method
company_responseYesCompany's response status
consumer_disputedNoWhether consumer disputed resolution
company_public_responseNoPublic response from company

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "company": {
+      "description": "Company name",
+      "type": "string"
+    },
+    "company_public_response": {
+      "description": "Public response from company",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "company_response": {
+      "description": "Company's response status",
+      "type": "string"
+    },
+    "complaint_id": {
+      "description": "Unique complaint identifier",
+      "type": "string"
+    },
+    "consumer_disputed": {
+      "description": "Whether consumer disputed resolution",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "date_received": {
+      "description": "Date complaint was received",
+      "type": "string"
+    },
+    "issue": {
+      "description": "Main complaint issue",
+      "type": "string"
+    },
+    "narrative": {
+      "description": "Consumer complaint narrative",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "product": {
+      "description": "Product category",
+      "type": "string"
+    },
+    "state": {
+      "description": "Consumer state",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "sub_issue": {
+      "description": "Subcategory of issue",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "sub_product": {
+      "description": "Subcategory of product",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "submitted_via": {
+      "description": "Submission method",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "timely": {
+      "description": "Whether response was timely",
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "complaint_id",
+    "date_received",
+    "product",
+    "issue",
+    "company",
+    "company_response"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "complaint_id": "3385723"
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, etc., covering safety. The description adds behavioral context about the returned fields (narrative, company response, resolution, metadata), which is valuable beyond annotations.

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

Conciseness5/5

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

Single sentence that front-loads the purpose and includes return details. Every word earns its place; no wasted information.

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 simplicity (1 param, output schema present), the description sufficiently covers purpose, usage context, and response contents. No gaps remain.

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% with a clear description for complaint_id. The description adds the phrase 'by ID', reinforcing the concept but not adding meaning beyond what the schema provides. Baseline 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?

Description clearly states the verb 'retrieve' and resource 'complaint by ID', specifies return contents (narrative, etc.), and distinguishes from sibling tools like cfpb_search_complaints which are about searching, not individual retrieval.

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 implies usage when a specific complaint ID is known, providing clear context. However, it does not explicitly state when not to use it or mention alternative tools for broader searches, which would improve guidance.

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
Disambiguation3/5

There are multiple overlapping tools for querying data (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, bet_research) that could confuse an agent, though descriptions are detailed enough to distinguish most. Tools like polymarket_edges and polymarket_edge_tracker are closely related, and ai_visibility_check vs scan_competitor_ai_presence overlap.

Naming Consistency2/5

Naming is highly inconsistent: some follow verb_noun (cfpb_search_complaints, resolve_entity, subscribe, recall), but many use varied patterns like adjectives (ai_visibility_check), imperative phrases (ask_pipeworx, scan_competitor_ai_presence), or compound/specialized names (polymarket_arbitrage, generate_llms_txt). No consistent prefix or convention is used across the toolset.

Tool Count2/5

With 36 tools spanning diverse domains (prediction markets, SEC filings, CFPB complaints, AI visibility, npm packages, IPC subscriptions), the server is sprawling and over-scoped. Many tools are specialized niche additions (polymarket_fill_risk, scan_dependency, generate_llms_txt) that expand the count without strong cohesion.

Completeness3/5

The toolset covers core areas well (entity resolution, profile, comparison, recent changes, search, claims verification, subscriptions). However, gaps exist: no update/delete for CFPB complaints (read-only), no direct raw SEC filing retrieval, and some lifecycle operations (e.g., editing subscriptions) are missing.

Resources