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Unilytics MCP Server

Unilytics MCP Server

Official
by TeamUnilytics
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JavaScript
Remote

append_skill_memory

Record one learning, workaround, or preference in a skill's memory to reuse what a run taught. Keeps the skill text unchanged and returns memory_full when the memory limit is reached.

Instructions

Add one entry (a learning, workaround or preference) to a skill's memory.

Use to record what a run of the skill taught: something that failed, a faster order, or a
preference the user stated. Works on built-in and Unilytics skills too: the entry goes to the
user's own memory of that skill and the skill text is unchanged. Returns memory_full once the
memory reaches its size limit; compact_skill_memory then condenses it.

Args:
    skill_id: The skill the learning belongs to.
    text: The learning/workaround/preference to remember (one concise entry).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "skill_id": "<skill_id>",
      "text": "<text>"
    },
    "name": "append_skill_memory"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
skill_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.1.0
    • removedInput schema / properties / config
      Removed value: -{
-  "default": null,
-  "title": "config",
-  "type": "string"
-}
  2. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations only declare the write profile (readOnly=false, destructive=false, idempotent=false); the description adds value beyond them by disclosing that it works on built-in and Unilytics skills, that the entry lands in the user's own memory while 'the skill text is unchanged,' and that a memory_full signal is returned at the size limit. No auth or rate-limit detail, but for a non-destructive append the disclosure is solid.

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?

Front-loaded with the action and purpose in the first sentence, then usage, scope caveat, and failure mode, with parameters last. The docstring-style 'Args:' block is somewhat boilerplate but earns its place given zero schema coverage; no sentence is redundant.

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?

No output schema exists, and the description compensates by naming the memory_full return condition and the compact_skill_memory handoff, plus the key behavioral fact that the skill definition is untouched. What remains thin is the skill_id identifier format, which is the one thing an agent could get wrong on the first call.

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 coverage is 0%, so the description must carry the burden; the Args block does explain both parameters ('The skill the learning belongs to', 'one concise entry'). However it never specifies the identifier format for skill_id (e.g. how a built-in skill is named) nor any length/format limit for text, leaving real ambiguity for a 2-param required write.

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?

States a specific verb and resource: 'Add one entry (a learning, workaround or preference) to a skill's memory.' It also scopes what an entry is, and implicitly separates itself from get_skill_memory (read) and compact_skill_memory (condense) by describing the append-only write.

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?

Gives concrete when-to-use criteria: 'record what a run of the skill taught: something that failed, a faster order, or a preference the user stated,' and names compact_skill_memory as the follow-up once memory_full is returned. It stops short of explicit exclusions (e.g. 'don't use this to edit the skill itself, use update_skill').

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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