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abhi-ramtel

mcp-overleaf-server

by abhi-ramtel

Plan a multi-job batch (saves reasoning passes)

batch_plan

Plan up to 10 job applications at once by clustering similar roles and checking cached content. Identifies exactly which jobs need fresh tailored content before writing.

Instructions

Plan up to 10 jobs at once. Deterministically clusters jobs by keyword similarity and checks a cross-session cache, then tells you EXACTLY which jobs need fresh TailoredContent. Similar roles share one reasoning pass — write content only for the listed indices, then call batch_render once. Call this BEFORE writing any content for a multi-job request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsYes
useCacheNoReuse content cached from previous sessions (default true)
thresholdNoSimilarity 0-1 above which jobs share content (default 0.65)
Behavior3/5

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

With no annotations, the description bears full burden. It discloses clustering, caching, and shared reasoning behavior, but does not mention the output format, side effects, or permissions needed. The agent would benefit from knowing what the tool returns and whether it is purely read-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?

The description is concise, with no redundant information. It front-loads the main purpose and function, then provides actionable steps for usage. Every sentence adds value.

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 no output schema or annotations, the description covers purpose, usage order, clustering logic, caching, and hints at output (which jobs need content). It is missing explicit output structure and error/edge case handling, but is otherwise complete for a planning tool.

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 67% (jobs parameter lacks description). The description compensates by explaining that jobs are clustered by keyword similarity and that roles share reasoning passes, adding meaning beyond the schema. The other parameters (useCache, threshold) are already described in the schema.

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's purpose: planning up to 10 jobs by clustering similar roles and checking a cache, then indicating which jobs need fresh content. It distinguishes itself from siblings like 'batch_render' by specifying that this tool is for planning before writing content.

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 explicitly instructs to call this tool before writing content for multi-job requests, and mentions that after planning one should call 'batch_render'. It implies it is for multiple jobs but does not explicitly exclude single-job scenarios or mention alternatives like 'prepare_tailoring'.

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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