job-mcp

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get_application_history

Retrieve historical job application submissions and outcomes from the audit ledger. Filter by status and limit results to review past successes, failures, or pending applications.

Instructions

Retrieve historical job application submissions and outcomes from the persistent audit ledger.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_application_history"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of records to return (default 50).
`status`	No	Optional status filter ('success', 'failed', 'blocked', 'pending').

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 5, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It adds useful context by naming a 'persistent audit ledger' (durable, historical source) and 'Retrieve' implies a read, but it does not state ordering, pagination semantics beyond limit, or whether results are scoped to a user. Partial disclosure only.

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?

A single front-loaded sentence with the verb, resource, and data source, and zero filler. Nothing could be trimmed without losing meaning.

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 simple two-parameter read tool with a full schema and an output schema, the description covers what the tool returns and where the data comes from. The main gap is the absence of when-to-use routing against siblings, which keeps it from a 5.

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 both 'limit' (default 50) and the optional 'status' filter with its allowed values. The description adds nothing about parameter syntax or behavior, so the baseline 3 applies.

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 (retrieve) and a precise resource (historical job application submissions and outcomes), which is inherently distinct from the apply/search siblings. It stops short of explicitly differentiating itself from tools like mark_job_as_applied or search_linkedin_jobs, so it earns a 4 rather than a 5.

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?

There is no guidance on when to reach for this tool versus the many siblings that also touch applications, nor any stated prerequisites or exclusions. The purpose implies a reporting/audit use case, but the agent must infer it entirely.

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