fdic_get_institutionGet detailed profile for an FDIC-insured bank (e.g., CERT "5136"). Returns name, location, assets, deposits, and regulatory details.
| Name | Required | Description | Default |
|---|---|---|---|
| cert | Yes | FDIC certificate number (e.g., "628" for Chase) |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Institution details from FDIC database",
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "cert": "628"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds that it returns a detailed profile with specific fields, but does not disclose any additional behaviors (e.g., rate limits, authentication needs). It does not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence (19 words) that is front-loaded with the core action and resource, followed by a parenthetical example and return content. Every word adds value; there is no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one required parameter, output schema present, and annotations covering safety), the description provides sufficient context: it states what the tool does, what it returns, and includes an example. No additional information is necessary for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers the sole parameter 'cert' with 100% description coverage, including an example. The tool description provides a similar example but adds no new meaning beyond what the schema already provides. Thus, the description does not enhance parameter understanding beyond baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('detailed profile for an FDIC-insured bank') with concrete examples and return fields. It clearly distinguishes from sibling tools like fdic_search_institutions (which is for searching) and fdic_financials (financial data), as it focuses on a full profile by certificate number.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a specific bank's cert number is known, but it does not explicitly state when to use this tool versus siblings (e.g., fdic_search_institutions for search, fdic_failures for failed banks). No exclusions or prerequisites are mentioned, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.