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Cfpb

by io.github.pipeworx-io

Cfpb Company Complaints

cfpb_company_complaints
Read-onlyIdempotent

Get recent complaints against a specific company (e.g., 'Wells Fargo', 'Citibank'). Everyday names are resolved to CFPB's exact registered spelling automatically ("Wells Fargo" -> "WELLS FARGO & COMPANY"), and the response reports which name was actually queried. Returns narratives, company responses, and resolution details sorted newest first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results (1-100, default 25)
companyYesCompany name (e.g., "BANK OF AMERICA", "CITIBANK", "JPMORGAN CHASE")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesTotal complaints against company
companyYesCompany name searched
complaintsYesList of complaint records

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "company": "BANK OF AMERICA",
-    "limit": 25
-  },
-  {
-    "company": "JPMORGAN CHASE",
-    "limit": 50
-  }
-]New value: +[
+  {
+    "company": "Wells Fargo",
+    "limit": 25
+  },
+  {
+    "company": "JPMORGAN CHASE",
+    "limit": 50
+  }
+]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "company": {
+      "description": "Company name searched",
+      "type": "string"
+    },
+    "complaints": {
+      "description": "List of complaint records",
+      "items": {
+        "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"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Total complaints against company",
+      "type": "number"
+    }
+  },
+  "required": [
+    "company",
+    "total",
+    "complaints"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "company": "BANK OF AMERICA",
+    "limit": 25
+  },
+  {
+    "company": "JPMORGAN CHASE",
+    "limit": 50
+  }
+]
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations mark the tool as read-only and safe, and the description adds behavioral context: it explains automatic normalization of common names to registered spelling, that the response will indicate the actual queried name, and that output includes narratives, company responses, and resolution details sorted newest first. This goes beyond basic safety to reveal how results may differ from the input.

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?

The description is two sentences long with no redundant phrases. The first sentence provides an action-oriented summary; the second adds the most critical behavioral nuance (name resolution) and output details, each earning its place.

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?

Given the robust annotations, output schema, and simple parameter set, the description covers the key use case. It could be enhanced by explicitly pointing to sibling tools (e.g., cfpb_search_complaints for broader searches), but it is still complete enough to select and invoke the tool correctly.

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?

Both parameters are already described in the schema (100% coverage), so the baseline is 3. The description enriches the company parameter by revealing that everyday names are resolved automatically and that the response reports the actual queried name, which informs how to construct input. The limit parameter is sufficiently covered by 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 a specific verb and resource: 'Get recent complaints against a specific company'. It provides concrete examples and clarifies the scope versus siblings (company-specific rather than generic search). It also mentions unique features like automatic name resolution, distinguishing it from cfpb_search_complaints or cfpb_get_complaint.

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 clearly implies the tool should be used when the agent needs complaints for a named company, and even gives example everyday names. However, it does not explicitly mention alternatives or state when not to use this tool, leaving a slight gap in exclusivity 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