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Costory: Your Finops MCP

by io.costory.app-api
OverviewDiscussionsAdmin

search_documentation

Read-only

Search Costory product docs (Mintlify) and knowledge base in parallel. Use when the user asks how Costory works or needs product guidance. Results identify product documentation separately from knowledge-base articles. Returns Mintlify matches (titles, snippets, and full docs URLs (Url: https://docs.costory.io/...)) and KB articles (title, summary, full markdown). Optional limit (1–10, default 5) applies to KB. For a full Mintlify page, use get_documentation_page. When citing a page in chat, use the full Url: value verbatim as the markdown href — do not convert to a relative app path. EXAMPLES: • "How do I create a budget alert?" → { query: "budget alert" } • "Why do costs differ from AWS Cost Explorer?" → { query: "AWS Cost Explorer discrepancy", limit: 3 }

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>",
      "context": "<context>"
    },
    "name": "search_documentation"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax number of results to return (1–10, default 5)
queryYesSearch term to find in documentation (e.g. 'budget alerts', 'dimensions', 'API reference')
contextYesA few words on why you are calling this tool, for the server developer's analytics. Describe people and things by role, such as "a customer". Do not quote the user or include names, emails, or other personal details.
llm_modelNoThe model identifier you are running as, for example "claude-opus-4-8" or "gpt-5.2". Pass "unknown" if you are not certain. Recorded for the server developer's analytics.
conversation_idNoThe conversation_id this server returned earlier in this conversation, unchanged, including after later user messages. Omit it if you have none. Recorded for the server developer's analytics.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / context / description
      Previous value: -"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""New value: +"A few words on why you are calling this tool, for the server developer's analytics. Describe people and things by role, such as \"a customer\". Do not quote the user or include names, emails, or other personal details."
    • changedInput schema / required
      Previous value: -[
-  "query",
-  "context",
-  "llm_model"
-]New value: +[
+  "query",
+  "context"
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "The conversation_id this server returned earlier in this conversation, unchanged, including after later user messages. Omit it if you have none. Recorded for the server developer's analytics.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The model identifier you are running as, for example \"claude-opus-4-8\" or \"gpt-5.2\". Pass \"unknown\" if you are not certain. Recorded for the server developer's analytics.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "query"
-]New value: +[
+  "query",
+  "context",
+  "llm_model"
+]
  3. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already establish readOnly and non-destructive, so the description wisely spends its words on behavior: parallel search of two sources, result shape per source (Mintlify titles/snippets/URLs vs KB title/summary/full markdown), and the asymmetric limit scope. It does not cover ranking, latency, or empty-result behavior, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Front-loaded with purpose, then usage, then return shape, then the sibling handoff, then examples — a logical order with no filler. The citation instruction about using the full `Url:` verbatim is slightly verbose but prevents a real failure mode, so it earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and does so fully: it specifies both result sets, the fields in each, and the URL format to cite. An agent can call this correctly and cite results properly without opening anything else.

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?

Schema coverage is 100%, so the baseline is 3; the description earns an extra point by clarifying that limit (1–10, default 5) applies only to KB results, a constraint the schema does not state. It also demonstrates limit usage in an example, though the analytics-only params (context, llm_model, conversation_id) get no prose treatment.

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?

'Search Costory product docs (Mintlify) and knowledge base in parallel' names a specific verb, the exact resources, and the parallel-fan-out behavior. It is immediately distinguishable from get_documentation_page, which the description explicitly routes to for full pages.

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

Usage Guidelines5/5

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

States the triggering condition ('user asks how Costory works or needs product guidance'), names the alternative (get_documentation_page) with its selection condition, and backs both with two concrete query examples including one with a limit override.

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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