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JinyangWang27

people-context

withdraw_candidates

Remove staged candidates from a batch so they are not committed. Withdrawn candidates stay marked as rejected, letting you review dropped items while preserving the rest of the batch.

Instructions

Drop staged candidates from consideration, returning the revised batch.

A withdrawn candidate stays listed as rejected so the user can see what was dropped, and is never committed. Nothing else about the batch changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batch_idYes
candidate_idsYes
expected_batch_digestNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.3.0

TDQS

A3.8/5.0
Behavior4/5

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

The annotations already indicate this is not read-only, not idempotent, and not destructive, but the description adds valuable specifics: withdrawn candidates remain listed as 'rejected' and are never committed, and nothing else changes. This goes beyond the annotations by clarifying the exact side effect on the batch, which is essential for an agent to understand the tool's impact.

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 two sentences with no fluff. The primary action is front-loaded, and the second sentence adds essential behavioral details without redundancy. Every word earns its place, making it highly efficient for an agent to parse.

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?

While an output schema exists (so return format is covered), the description leaves the optional expected_batch_digest parameter undocumented. The main behavior is well explained, but an agent may not know why the digest parameter exists or how to use it. Given the tool's relative simplicity and the presence of an output schema, the description is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must explain parameter meanings. It does not mention batch_id, candidate_ids, or expected_batch_digest at all. While batch_id and candidate_ids are somewhat self-explanatory from their names, the optional expected_batch_digest is completely unexplained, leaving a significant gap for an agent trying to decide whether to provide it or what it should contain.

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 verb 'Drop' and the resource 'staged candidates', and mentions the result 'returning the revised batch'. It also adds a distinctive behavioral detail (candidates become 'rejected' and are never committed) that distinguishes it from sibling tools like stage_candidates or commit_import. This makes the purpose unambiguous.

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?

The description implies usage for removing candidates before a commit, but it does not explicitly state when to use this tool versus alternatives such as amend_candidate or commit_import. It gives behavioral context that helps infer usage, but there is no explicit guidance on exclusions or alternative selection. This leaves some ambiguity for an agent.

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