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Glama

Cfpb

by io.github.pipeworx-io

Cfpb Product Breakdown

cfpb_product_breakdown
Read-onlyIdempotent

Get complaint counts by product category (e.g., 'Credit Card', 'Mortgage'). Filter by company or date range.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNoOptional company name to filter by
end_dateNoEnd date in YYYY-MM-DD format
start_dateNoStart date in YYYY-MM-DD format

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersYes
productsYesComplaint counts by product category
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": {
+        "company": {
+          "description": "Company filter applied",
+          "type": [
+            "string",
+            "null"
+          ]
+        },
+        "date_range": {
+          "properties": {
+            "end": {
+              "description": "End date of range",
+              "type": [
+                "string",
+                "null"
+              ]
+            },
+            "start": {
+              "description": "Start date of range",
+              "type": [
+                "string",
+                "null"
+              ]
+            }
+          },
+          "type": "object"
+        }
+      },
+      "type": "object"
+    },
+    "products": {
+      "description": "Complaint counts by product category",
+      "items": {
+        "properties": {
+          "complaint_count": {
+            "description": "Number of complaints",
+            "type": "number"
+          },
+          "product": {
+            "description": "Product category name",
+            "type": "string"
+          }
+        },
+        "required": [
+          "product",
+          "complaint_count"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_complaints": {
+      "description": "Total complaints in period",
+      "type": "number"
+    }
+  },
+  "required": [
+    "filters",
+    "total_complaints",
+    "products"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "company": "WELLS FARGO"
+  },
+  {
+    "end_date": "2024-01-01",
+    "start_date": "2023-01-01"
+  }
+]
  4. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, so the safety profile is clear. The description adds that it retrieves counts, which is consistent. No additional behavioral details are needed for this simple read operation.

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 a single sentence with an example, very concise and front-loaded. Every word adds value, no fluff.

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?

For a low-complexity tool with 3 optional parameters, an output schema, and full annotation coverage, the description is mostly complete. It lacks mention of date dependency (e.g., both dates required together?) but overall is adequate.

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 each parameter described in the schema. The description reiterates filtering by company or date range but does not add new meaning beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns complaint counts by product category, with examples like 'Credit Card' and 'Mortgage'. It distinguishes from siblings like cfpb_company_complaints by focusing on product breakdowns, though it does not explicitly contrast with them.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like cfpb_company_complaints or cfpb_search_complaints. The description only states filtering options, not selection criteria, leaving the agent to infer context.

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