dsa-coach-mcp

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log_review

Records how well you recalled a problem, schedules the next spaced-repetition revision, and journals mistakes you made. Use it after each review to track recall and build a mistake log.

Instructions

Record how well the user recalled a problem (0 = could not, 5 = instantly), schedule the next revision, and journal any mistakes made on the way.

    Pass the problem's tags from search_problems or get_profile. Give each mistake a
    short kind, reusing an existing one from mistake_kinds when it fits, and a note
    on what went wrong in the user's own words.

Input Schema

Name	Required	Description	Default
`url`	Yes
`tags`	No
`title`	Yes
`quality`	Yes
`mistakes`	No
`platform`	Yes
`problem_id`	Yes

Output Schema

Name	Required	Description	Default
`due`	Yes
`url`	Yes
`ease`	Yes
`title`	Yes
`platform`	Yes
`problem_id`	Yes
`repetitions`	Yes
`interval_days`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 23, 2026

TDQS

A4/5.0

Behavior4/5

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

With annotations declaring readOnlyHint=false and idempotentHint=false, the mutating nature is known, but the description adds valuable context: it explains that a review schedules the next revision and journals mistakes, and defines the 0–5 quality scale. It does not discuss what happens on duplicate calls or auth requirements, leaving a minor gap.

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?

The description is three well-structured sentences, front-loading the main action and quality scale before parameter guidance. It is appropriately sized with little waste, though some sentences pack multiple instructions that could be slightly clearer.

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?

The tool is complex (7 parameters, 5 required) and an output schema exists, so return values needn't be explained. However, the description omits meaningful guidance on required fields like platform, problem_id, title, and url, which an agent needs to populate correctly. It covers the core action and a few parameters but is not fully complete 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 0%, so the description must compensate. It meaningfully explains quality (0–5 scale with anchor values), tags (source from search_problems/get_profile), and mistakes (kind and note, reuse from mistake_kinds), but leaves platform, problem_id, title, and url completely undocumented. Partial compensation is present but incomplete for 7 parameters.

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 uses specific verbs and resources: 'Record how well the user recalled a problem', 'schedule the next revision', and 'journal any mistakes'. This clearly distinguishes it from siblings like search_problems or revisions_due, and an agent can understand its distinct role without opening the schema.

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?

It explicitly advises passing tags from search_problems or get_profile and reusing mistake kinds from mistake_kinds, giving clear sourcing guidance. It implies the context (after a review session) but does not state when not to use it or name a direct alternative, so it falls short of the top score.

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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