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API Vault
by vinhtran1

execute_request

Sends HTTP, REST, or GraphQL requests with authentication to public, private, or local endpoints, then logs untruncated headers, bodies, and responses in local SQLite for searchable audit history.

Instructions

Execute an HTTP/REST/GraphQL request with unlimited audit logging into local SQLite database. Supports GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS. No size limits, 100% private.

Input Schema

NameRequiredDescriptionDefault
urlYesTarget URL (supports http, https, private hosts, localhost)
authNoAuthentication configuration
bodyNoRequest body (for POST, PUT, PATCH)
titleNoShort semantic title for the request
methodYesHTTP method
headersNoRequest headers (key-value pairs)
timeoutNoTimeout in milliseconds (default: 30000)
descriptionYesMANDATORY: Business context and explicit purpose explaining why this request is called

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose two genuinely useful traits — every request is persisted to a local SQLite audit log (a durable side effect) and traffic stays local ('100% private') — but says nothing about whether mutating methods like DELETE take real effect, how auth failures surface, or what a response looks like.

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?

Two short sentences, front-loaded with the action and its persistence behavior. The trailing 'No size limits, 100% private' is slightly promotional but still carries operational information.

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

Completeness3/5

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

For an 8-parameter tool with a nested auth object, no annotations, and no output schema, the description leaves real gaps: return value shape, error behavior, and how the auth variants (bearer/basic/api-key, header vs query) behave are undocumented anywhere. Adequate but incomplete for this complexity.

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 documents url, method, auth, headers, body, timeout, and the mandatory business-context field. The description only restates the method list (already an enum) and adds 'no size limits' for bodies; it adds no meaning for the nested auth object or timeout.

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?

States a specific verb and resource — execute an HTTP/REST/GraphQL request — plus the side effect of audit logging to local SQLite. Sibling tools (search_history, get_request_detail) are clearly read-only history tools, so the executor role is distinguishable, though the description never names them explicitly.

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?

Usage is only implied: the tool is the way to make outbound calls, and the mention of audit logging hints that results are later visible via search_history, but no when-to-use/when-not-to-use conditions or alternatives are stated. An agent must infer the boundary itself.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.