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Cfpb

by io.github.pipeworx-io

Cfpb Top Companies

cfpb_top_companies
Read-onlyIdempotent

Find companies with the most complaints in a date range. Returns ranked list with company names and complaint counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of top companies to return (default 10)
productNoOptional product filter (e.g., "Mortgage", "Credit card")
end_dateNoEnd date in YYYY-MM-DD format
start_dateNoStart date in YYYY-MM-DD format

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersYes
top_companiesYesRanked list of companies by complaint count
total_complaintsYestotal_complaints (object for aggregations, number for scalar count).

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedOutput schema / properties / total_complaints / description
      Previous value: -"Total complaints in period"New value: +"total_complaints (object for aggregations, number for scalar count)."
    • changedOutput schema / properties / total_complaints / type
      Previous value: -"number"New value: +[
+  "object",
+  "number"
+]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "filters": {
+      "properties": {
+        "date_range": {
+          "properties": {
+            "end": {
+              "description": "End date of range",
+              "type": [
+                "string",
+                "null"
+              ]
+            },
+            "start": {
+              "description": "Start date of range",
+              "type": [
+                "string",
+                "null"
+              ]
+            }
+          },
+          "type": "object"
+        },
+        "product": {
+          "description": "Product filter applied",
+          "type": [
+            "string",
+            "null"
+          ]
+        }
+      },
+      "type": "object"
+    },
+    "top_companies": {
+      "description": "Ranked list of companies by complaint count",
+      "items": {
+        "properties": {
+          "company": {
+            "description": "Company name",
+            "type": "string"
+          },
+          "complaint_count": {
+            "description": "Number of complaints",
+            "type": "number"
+          },
+          "rank": {
+            "description": "Ranking position",
+            "type": "number"
+          }
+        },
+        "required": [
+          "rank",
+          "company",
+          "complaint_count"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_complaints": {
+      "description": "Total complaints in period",
+      "type": "number"
+    }
+  },
+  "required": [
+    "filters",
+    "total_complaints",
+    "top_companies"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "end_date": "2024-01-01",
+    "limit": 10,
+    "start_date": "2023-01-01"
+  },
+  {
+    "end_date": "2024-06-01",
+    "limit": 15,
+    "product": "Mortgage",
+    "start_date": "2023-06-01"
+  }
+]
  4. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that output is a ranked list with company names and counts, which is useful but does not disclose potential pagination, maximum limit, or error conditions. 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.

Conciseness5/5

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

Two sentences with no redundancy. First sentence states action and scope; second sentence specifies output. Every word is necessary and front-loaded.

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 that output schema exists and annotations are comprehensive, the description is nearly complete. It covers the core purpose and result format. Minor improvement would be to explicitly mention descending order by count, but 'most complaints' implies ranking, so this is sufficient.

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 description coverage is 100%, so the schema already describes all parameters. Description reinforces that date range is used (start_date, end_date) but does not add new meaning beyond stating the tool's purpose. 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 uses specific verb 'find', identifies resource 'companies with the most complaints', and specifies time scope 'in a date range'. It clearly differentiates from siblings like cfpb_search_complaints (search individual complaints) and cfpb_product_breakdown (breakdown by product) by focusing on ranking companies by complaint volume.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description does not explicitly state when to use this tool versus alternatives or provide exclusions. The purpose is implied but lacks guidance such as 'use this to identify top complaint recipients' or 'for individual complaint details, use cfpb_get_complaint'.

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.

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