lawmem-ai/lawmem-mcp

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

No annotations provided, so description carries full burden. Excellent disclosure: specifies hard delete nature, dual storage removal (vector store and database), legal compliance context (GDPR), and irreversibility. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each earning its place: (1) core action + UUID identifier, (2) legal compliance context, (3) technical implementation details, (4) irreversibility warning. Front-loaded with essential action. No redundancy.

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

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

Comprehensive for a destructive operation despite lack of annotations and output schema. Covers legal implications, data scope (both stores), and irreversibility. Minor gap: could mention parameter source/origin, but adequate for safe invocation.

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

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

Schema has 0% description coverage for the single required parameter. Description compensates partially by specifying the memory_id expects a UUID format. However, it omits where to obtain this ID (e.g., from store_memory or recall_memories) or what it represents beyond the obvious.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verb (delete) + resource (memory) + scope (permanent/hard delete). Clearly distinguishes from siblings get_stats, recall_memories, and store_memory by being the only destructive removal operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear when-to-use context via GDPR right-to-erasure compliance mention. Includes explicit irreversibility warning ('cannot be undone') implying when-not-to-use. Lacks explicit naming of sibling alternatives (e.g., 'use recall_memories to view instead').

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

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

No annotations provided, so description carries full burden. It discloses what data is returned (compensating for missing output schema), but omits behavioral traits like whether calls consume quota, cache behavior, or if data is real-time vs. aggregated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence efficiently lists all return values without redundancy. Front-loaded with action verb, compact enumeration of statistics, zero wasted words.

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

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

For a zero-parameter tool, adequately compensates for missing output schema by enumerating specific return values. Lacks only behavioral context (cost, caching) to be fully complete.

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

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

Zero parameters with 100% schema coverage (empty object). Baseline score applies as there are no parameters requiring semantic clarification beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Uses specific verb 'Get' with resource 'usage statistics' and scope 'for your tenant'. Lists concrete return values (memory count, API calls, wallet balance) that clearly distinguish it from sibling memory-manipulation tools (delete_memory, store_memory, recall_memories).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies monitoring/inspection usage through the statistics listed, but provides no explicit when-to-use guidance, prerequisites, or comparisons to siblings (e.g., when to check stats vs. perform operations).

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

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

With no annotations provided, the description carries the full burden and successfully discloses key behaviors: ranking algorithm (cosine similarity), output structure (text preview, score, metadata), and search methodology (natural language/semantic). Could mention idempotency or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences with zero waste: sentence 1 defines purpose, sentence 2 explains ranking, sentence 3 documents the key optional parameter, sentence 4 describes return values. Well front-loaded and appropriately sized.

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

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

Given zero annotations, no output schema, and 0% schema coverage, the description admirably compensates by detailing the return structure and ranking behavior. Missing only minor details like explicit top_k documentation or error conditions.

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

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

Schema has 0% description coverage, but the description compensates well by explaining 'query' (natural language input) and 'matter_id' (case filtering). Only 'top_k' is not explicitly addressed, though implied by 'most relevant results'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the specific action ('Semantically search') and resource ('stored legal memories'), distinguishing it from siblings: store_memory (write), delete_memory (delete), and get_stats (metrics).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides implicit guidance by explaining the matter_id filter ('restrict results to a specific case'), but lacks explicit when-to-use/when-not-to-use comparisons with sibling tools like store_memory or delete_memory.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully explains the embedding mechanism ('nomic-embed-text'), storage backend ('vector database'), and return values ('memory ID, token count, and a preview'), which are critical for a storage operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences each serve distinct purposes: purpose statement, technical implementation, return value disclosure, and parameter guidance. No redundancy or filler; information is front-loaded with the core action.

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

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

Given the absence of both schema descriptions and output schema, the description adequately compensates by explaining return values and the required 'text' parameter. However, it omits explanations for three optional parameters (agent_id, metadata, source_doc), preventing a perfect score.

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

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

Schema description coverage is 0%, requiring the description to compensate. It adds semantic meaning for matter_id (scoping to cases) and implicitly for text (the content being embedded), but provides no explanation for agent_id, metadata, or source_doc, leaving three optional parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Store') and resource ('legal text memory'), and clarifies the intent ('for later semantic recall'). This clearly distinguishes it from sibling tools delete_memory (removal) and recall_memories (retrieval).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage guidance for the matter_id parameter ('Use matter_id to scope memories to a specific case'), but lacks explicit guidance on when to choose this tool over recall_memories or delete_memory, and does not state prerequisites or exclusions.

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