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Fdic

by io.github.pipeworx-io

Fdic Search Institutions

fdic_search_institutions
Read-onlyIdempotent

Search FDIC-insured banks by name. Returns institution name, CERT ID, location, total assets, deposits, net income, ROA, ROE, and report date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results to return (default 10)
searchYesBank or institution name to search for (e.g., "Chase", "Wells Fargo")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query string
institutionsYesList of institutions matching the search
total_resultsYesTotal number of matching institutions

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": {
+    "institutions": {
+      "description": "List of institutions matching the search",
+      "items": {
+        "properties": {
+          "cert": {
+            "description": "FDIC certificate number",
+            "type": [
+              "string",
+              "number",
+              "null"
+            ]
+          },
+          "city": {
+            "description": "City location",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "name": {
+            "description": "Institution name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "net_income": {
+            "description": "Net income in dollars",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "report_date": {
+            "description": "Report date",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "roa": {
+            "description": "Return on assets ratio",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "roe": {
+            "description": "Return on equity ratio",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "state": {
+            "description": "State code",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "total_assets": {
+            "description": "Total assets in dollars",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "total_deposits": {
+            "description": "Total deposits in dollars",
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "query": {
+      "description": "Search query string",
+      "type": "string"
+    },
+    "total_results": {
+      "description": "Total number of matching institutions",
+      "type": "number"
+    }
+  },
+  "required": [
+    "query",
+    "total_results",
+    "institutions"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "search": "Chase"
+  },
+  {
+    "limit": 5,
+    "search": "Wells Fargo"
+  }
+]
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, openWorld, and non-destructive behavior. The description adds value by listing specific return fields, enhancing behavioral understanding. 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?

The description is a single, well-structured sentence that front-loads the purpose and then lists return fields, all without unnecessary words. Every sentence earns 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 a simple search tool with two parameters and an output schema present, the description covers the essential behavior. It could mention pagination or default limit, but the schema already defines limit. Overall 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%, so parameters are fully described. The description mentions searching 'by name', which aligns with the 'search' parameter, but adds no additional nuance beyond the schema. The 'limit' parameter is not discussed. Baseline score of 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?

The description clearly states the tool searches FDIC-insured banks by name and lists the return fields, effectively distinguishing it from siblings like fdic_failures or fdic_get_institution which have different purposes.

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 when to use (searching banks by name) and is clear about the return fields, but does not explicitly state when not to use or compare with alternatives. Sibling tools like fdic_get_institution provide additional context for differentiation.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions over the same underlying data catalog, and the five polymarket_* tools plus bet_research all analyze prediction-market opportunities. Tools like entity_profile, recent_changes, compare_entities, and resolve_entity also blur together for company research.

Naming Consistency2/5

Names are all snake_case but follow no consistent convention: some are bare verbs (forget, recall, remember, subscribe), some are noun phrases (fdic_failures, entity_profile, pipeworx_trending), and some are verb_noun (fdic_get_institution, generate_llms_txt, validate_claim). Even the fdic_* family mixes noun-only and verb_noun styles.

Tool Count2/5

At 36 tools this exceeds the 25-tool threshold for 'too many.' The count is inflated by redundant meta-tools (three ask_pipeworx variants, discover_tools, suggest_questions, multiple polymarket scanners) and unrelated purpose tools (generate_llms_txt, scan_dependency, ai_visibility_check) that do not belong in an FDIC-named server.

Completeness3/5

The universal ask_pipeworx router gives broad data coverage for almost any factual question, so core lookups are unlikely to dead-end. However, the FDIC-specific surface is thin (only five tools, missing branch/geography/history data), and the server's actual scope is so broad and mixed that no single domain is fully covered end-to-end.

Resources