Server Quality Checklist

Tool Scores

• memory_clearA
  4.4/5.0
  Behavior5/5

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

  Beyond the annotations (destructiveHint, idempotentHint), the description discloses the file-removal mechanism, irreversibility, the lease-refusal behavior, and the known residual race condition. This is substantial behavioral context that helps an agent predict edge-case behavior and safety trade-offs.

  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 front-loaded with the core action and irreversibility in the first sentence. The later sections on lease handling and race conditions are verbose but each sentence adds meaningful safety context. It is not maximally concise but well-organized.

  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?

  Given the tool's destructive and concurrency-sensitive nature, the description fully covers normal operation, edge cases (missing namespace, live lease), isolation guarantees, and known limitations. The output schema can handle return-value details, so the description is complete enough for safe invocation.

  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?

  The input schema already fully documents the 'namespace' parameter with 100% coverage, including its meaning as an isolation key and file path. The description adds no further parameter-specific detail, so the baseline score of 3 is appropriate.

  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 first sentence states a specific action—deleting ALL memory for a namespace by removing its SQLite file—and clearly marks it as irreversible, distinguishing it from per-fact deletion (explicitly disclaimed) and sibling tools like memory_add or memory_search.

  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?

  The description gives explicit when-to-use ('Use only when the namespace's entire history should be forgotten') and when-not-to-use ('there is no per-fact deletion'), but does not name an alternative sibling tool. It also clarifies idempotency and namespace isolation, but stops short of referencing a specific replacement tool.

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

• memory_maintenance_statusA
  4.4/5.0
  Behavior5/5

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

  Beyond the readOnlyHint and openWorldHint annotations, it discloses that the tool reads the namespace DB directly, never builds the embedder/reranker, and does not call _mem(). This explains the performance and safety characteristics.

  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 two sentences: the first front-loads the core purpose, the second adds a critical behavioral guarantee. It is dense but every sentence contributes value.

  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?

  With one well-documented parameter, a detailed schema, and an output schema present, the description covers the essential usage context and an important behavioral constraint. No gaps are apparent.

  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?

  The schema already provides a rich description of the namespace parameter (100% coverage), and the description adds no additional parameter-specific meaning beyond referencing 'namespace' in the main text. Baseline 3 is appropriate.

  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 a specific verb ('Report') and names the resource ('maintenance ledger') with details ('runs + pending proposals'), clearly distinguishing it from siblings like memory_maintenance_run. It references section §6.3, adding precision.

  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 positions the tool as MODEL-FREE by contract, explaining that checking maintenance status should never trigger a ~2 GB model download. This implies when to prefer this tool, though it doesn't name alternative tools explicitly.

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

• memory_review_decideA
  4.5/5.0
  Behavior5/5

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

  The description provides detailed behavioral semantics for each decision: 'approve applies the proposal's verbs at decide-time (with apply-time target re-validation)', 'reject leaves the spine byte-identical', and 'edit'/'promote' specifics. This goes well beyond the sparse annotations (all false) and clarifies that while not read-only, reject is non-destructive to the spine. It also discloses the return type.

  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 three sentences: the first states the core purpose and options, the second details each option's effect, and the third states the return type. There is no filler or redundancy, and the structure front-loads the most important information.

  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?

  The description covers all decision types and their effects, and the output schema covers return details. Parameters are well documented in the schema (100% coverage). The only minor gap is not explicitly stating the need for edited_text with edit, but that is in the schema.

  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 100%, so baseline is 3. The description's explanation of each decision adds nuance (e.g., 'edit (summarize only)') beyond the schema's enum constraints, but it doesn't add syntax or required-field details; these are fully covered by the schema.

  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 opens with 'Decide a maintenance proposal: approve | reject | edit | promote (§6.3)', clearly identifying the verb and resource and distinguishing it from queue/run siblings by listing the four decision actions. It also specifies the effect of each decision, making the tool's 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 Guidelines4/5

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

  The description explains the semantics of each decision option but does not explicitly state when to use this tool versus alternatives like memory_review_queue or memory_maintenance_run. The schema's proposal_id reference to memory_review_queue provides indirect context, but the description itself lacks explicit when-to-use guidance.

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

• memory_review_queueA
  4.5/5.0
  Behavior5/5

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

  The description proactively discloses the non-read-only nature: 'Not fully read-only: overdue proposals lazily expire (are marked expired) as a side effect of listing.' It also clarifies the return type (JSON string). This goes beyond the annotations, which only note readOnlyHint=false, and provides essential context without contradiction.

  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 three sentences, front-loaded with the primary purpose, then adds critical side-effect information and a pointer to the next tool. Every sentence contributes value and there is no redundancy or filler, making it highly efficient.

  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?

  Given the presence of an output schema and well-documented parameters, the description covers all necessary aspects: what it lists, how the data is structured, the side effect, the output type, and the relationship to sibling tools. It is complete for this list-with-side-effect utility.

  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 100%, and each parameter (kind, limit, namespace) already has a clear description in the schema. The tool description adds no additional parameter-level detail beyond the context of what the listing returns, so it meets the baseline but does not exceed it.

  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 opens with a specific verb and resource: 'List pending maintenance proposals, grouped by entity, with evidence.' It clearly distinguishes from siblings by mentioning the follow-up tool memory_review_decide for acting on proposals, making 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 Guidelines4/5

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

  The description provides context for use (reviewing pending proposals before deciding) and explicitly directs to memory_review_decide for the next step, which acts as an alternative. However, it does not explicitly state when not to use this tool or mention other alternatives like memory_maintenance_status, so it falls short of full explicit usage guidance.

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

• memory_searchA
  4.5/5.0
  Behavior5/5

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

  Annotations already declare readOnlyHint=true, but the description adds a crucial caveat: searching a non-existent namespace returns 'No facts found.' yet creates an empty store file as a side effect. It also discloses deduplication, relevance ordering, and the potential one-time model weights download that blocks the call, going well beyond the annotations.

  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 thoughtfully structured into three short paragraphs: what it does, read-only nature and return format, and usage context with caveats. Every sentence adds value, and it remains readable despite covering several subtle points.

  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?

  With an output schema present and rich annotations, the description still adds essential context: when to use, how results are ordered and deduplicated, side effects, and interaction with sibling tools. Nothing critical is missing for this read-only search tool.

  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?

  The schema already covers 100% of parameters with rich descriptions (namespace isolation, query matching, k as top-k after reranking). The tool description adds the return format and deduplication behavior, but this is largely complementary to the schema rather than adding new semantic meaning to the parameters themselves.

  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 opens with a specific verb+resource pair: 'Retrieve the facts most relevant to a query from a namespace's memory.' This clearly distinguishes it from sibling tools like memory_add and memory_clear, which write or delete facts.

  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 says to use this tool before answering anything that may depend on prior context, and contrasts it with memory_add and memory_clear to clarify how facts enter and leave the store. It does not list explicit exclusions, but the context is clear enough.

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

• memory_addA
  4.7/5.0
  Behavior5/5

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

  Beyond annotations, the description states 'Additive only — never overwrites or deletes existing facts; contradictions are handled by supersession, with full history retained.' It also notes namespace creation on first write and the one-time model download that blocks, providing substantial behavioral context that annotations do not cover.

  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 organized into three short paragraphs: purpose, behavior, and usage context. Every sentence contributes unique value (e.g., return count, supersession, model download) and the core action is front-loaded. No waste or repetition.

  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?

  Even with an output schema present, the description explains the return value's meaning (count of facts written) and covers key side effects such as namespace creation, supersession, and blocking model download. This is sufficient for an agent to invoke the tool correctly.

  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 100%, and both text and namespace already have detailed descriptions in the schema. The description restates the distillation behavior already present in the schema without adding new parameter-specific semantics, so the baseline of 3 is appropriate.

  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 opens with 'Distill durable facts from text and write them to a namespace's memory,' which clearly states a specific action and resource. It contrasts with siblings by focusing on adding facts, and later mentions memory_search and memory_clear as distinct alternatives.

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

  Usage Guidelines5/5

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

  The description explicitly says 'Use it after learning durable information worth recalling in later sessions (preferences, decisions, biographical facts) — not for transient chatter, and not to re-state facts already in memory,' and names alternatives with 'use memory_search to check what is already known; use memory_clear to delete a namespace.' This provides clear when/when-not guidance.

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

• memory_maintenance_runA
  4.8/5.0
  Behavior5/5

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

  It discloses key behavioral details: dry-run performs zero writes, apply=True claims a lease, runs a provably-safe auto band, and stages proposals. It also notes the important asymmetry that only interactive apply=True stages proposals, which is valuable context beyond the annotations.

  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 logically structured with clear paragraphs and a note section. It is slightly verbose but every sentence carries meaningful operational detail, so the length is justified.

  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?

  Given the tool's complexity, the description covers all essential aspects: modes, lease behavior, proposal staging, comparison to auto-spawn, and return summary. The output schema is present, and the description still summarizes return values, making it complete for an AI agent.

  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?

  The input schema already provides 100% parameter coverage with detailed descriptions. The tool description adds extra context by explaining the dry-run/apply distinction and the maintenance lease behavior, but it mostly reinforces rather than substantially extends the schema's parameter semantics.

  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 states a specific verb and resource: 'Run one sleep-time maintenance pass on a namespace', which clearly defines the tool's function. It further distinguishes behavior by explaining dry-run vs. apply modes and notes symmetry with `lean-memory-maintain`, separating it from sibling status tools.

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

  Usage Guidelines5/5

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

  The description explicitly explains when to use dry-run (default) versus apply=True, and warns about the LM_MAINT_AUTO auto-spawn path, noting that unattended runs never stage proposals. This gives clear when/when-not guidance and references an alternative invocation path.

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