mcp-memory

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pruefe

Compare two or more stored memory entry IDs with a small language model to flag likely contradictions, progress, or disputed relations, showing where to inspect further.

Instructions

Has a small language model judge how the named entries relate to each other.

Counterpart to verdichten (consolidate): that one finds groups by word overlap, this one says whether a contradiction is behind it or just a newer state of affairs. All pairs from the named ids are judged.

Does NOT judge authoritatively. Measured against 70 pairs: 67 % correct, 9 % false contradiction alarms, but only 2 of 6 real ones caught - and the model says FORTSCHRITT (progress) in 50 of 70 cases. The result says where to look closer, not what the actual case is.

Each pair is asked in BOTH directions, and only what comes back the same both times is reported; otherwise it says "uneinig" (disputed/unclear). Without this, the order of the supplied ids would influence the outcome (measured: 2 of 5 pairs were symmetric). This costs double, i.e. roughly 10 seconds per pair, which is why the cap is 5 pairs asked per call.

Opt-in: needs MEMORY_FC_URL and MEMORY_FC_MODELL pointing at any OpenAI-compatible endpoint (local model, LAN device, or cloud provider; MEMORY_FC_SCHLUESSEL for providers that want a key). Without both set, this tool does nothing and says so - there are no defaults, so nothing is contacted unless it was set up deliberately. Note that when it IS set up, up to 200 characters of each note are sent to that endpoint. If it is unreachable, the tool says so and returns nothing - all other tools are unaffected.

Args: ids: Two or more entry ids, e.g. "249,255".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ids`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 19, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so richly: measured accuracy (67% correct, 9% false alarms), a stated bias toward FORTSCHRITT, bidirectional judging with 'uneinig' on disagreement, ~10s/pair cost, a 5-pair cap, and the privacy disclosure that up to 200 chars per note are sent to the endpoint. Failure behavior (unreachable endpoint returns nothing, other tools unaffected) is also disclosed.

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 core purpose and sibling contrast, and every later paragraph carries load-bearing information (accuracy, cost, opt-in, privacy). It is long, but the length is justified by the tool's caveats rather than padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, and the description still clarifies result semantics ('where to look closer', 'uneinig'). Combined with the cost, cap, opt-in, and failure-mode coverage, an agent has everything needed to decide and call correctly.

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 description coverage is 0% for the single 'ids' parameter, so the description must compensate and largely does: it gives the format ('249,255') and the minimum cardinality ('Two or more entry ids'). It could be more explicit that the value is a comma-separated string, so it is not quite perfect.

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: it has a language model judge how named entries relate. It explicitly positions itself against the sibling verdichten ('that one finds groups by word overlap, this one says whether a contradiction is behind it'), so an agent can distinguish the two without opening either 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?

Clearly differentiates from verdichten and states the opt-in precondition (needs MEMORY_FC_URL and MEMORY_FC_MODELL, otherwise does nothing). It implies the appropriate context but stops short of an explicit imperative 'use this when / not when' rule, so it lands just below the top.

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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