Server Quality Checklist

Tool Scores

• get_log_statsA
  3.8/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of disclosure. While 'aggregate stats' implies a read-only operation, it does not explicitly state that it does not modify logs, nor does it mention any permissions, error behavior, or performance characteristics beyond 'cheap'. Given the sibling tools include destructive actions, this omission is a significant gap.

  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 a single, concise sentence that front-loads the essential information ('Cheap aggregate stats') and lists the key stat categories. No unnecessary words or repetition.

  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?

  For a simple tool with one parameter, no output schema, and no annotations, the description covers the core purpose and what to expect. It could be more explicit about the exact structure of the returned stats, but it provides enough context for an agent to decide to use it.

  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 adds context by mentioning 'a ticket's ingested logs', which clarifies that ticket_id refers to a ticket that has logs. However, it does not describe the format or additional constraints beyond the schema's basic type. It provides minimal added meaning.

  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 clearly identifies the tool as providing aggregate stats for a ticket's ingested logs, specifying the types of stats (levels, errors, time span). It distinguishes from sibling tools like query_logs (raw logs) and ingest_ticket_logs (adding logs) by focusing on aggregate summaries rather than raw data.

  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 implies usage by saying 'cheap aggregate stats' which suggests it is a lightweight alternative to querying full logs, but it does not explicitly name alternatives or state when not to use it. It provides clear context but no exclusions.

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

• query_logsA
  3.9/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the full burden. It discloses that the tool fuses results via Reciprocal Rank Fusion, returns ranked chunks, and includes provenance metadata (source file, line range, time span, levels, trace ids). This is useful behavioral context beyond the schema, as it tells the agent what to expect in the response shape. However, it does not disclose potential rate limits, permission requirements, or whether the operation is read-only, but given no annotations this is a decent effort.

  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 compact: two sentences plus a brief list. It front-loads the core purpose and provides a clear list of metadata fields. No fluff or redundant phrasing. It earns its place by adding valuable context without being verbose.

  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?

  Given the tool has no output schema and 0% schema description coverage, the description does an adequate job explaining the retrieval logic and result metadata, but it lacks critical information about the ticket_id parameter's role and whether the query is natural language or keyword-based. It also does not mention pagination or result limits beyond top_k. For a retrieval tool with no annotations and no output schema, it leaves some gaps, but it covers the essential behavioral aspects.

  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 mentions the 'query' parameter implicitly but does not explain the syntax or meaning of 'ticket_id', 'top_k', or 'query' beyond what the schema provides (name, type, default). The description says 'Retrieval of the most relevant log chunks' which involves the 'query' but does not elaborate on how 'ticket_id' scopes the search (needed pre-requisite). The top_k default is in schema but not contextualized. The description adds some value (provenance metadata) but lacks per-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 clearly states it performs 'Hybrid semantic + keyword (BM25) retrieval of the most relevant log chunks.' It explicitly mentions combining dense vector similarity and BM25 lexical matching, which distinguishes it from siblings like ingest_ticket_logs (data ingestion) and delete_ticket_logs (data deletion). The verb 'query_logs' is specific to retrieval, making its 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 Guidelines3/5

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

  The description implies usage when the agent needs to retrieve relevant log chunks for a given ticket, but it does not explicitly state when to use it versus alternatives. It mentions 'most relevant log chunks' but does not specify conditions like 'use when you need to find logs without knowing exact filters' or contrast with get_log_stats (which likely provides aggregated views). No when-not-to-use guidance is provided.

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

• delete_ticket_logsA
  4.4/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the full burden. It discloses exactly what gets deleted: ticket vectors from the store and, by default, locally downloaded raw log files. It lacks explicit irreversibility or permission notes but is not misleading.

  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 two sentences, front-loaded with a clear summary and followed by concise elaboration. Every sentence adds value and there is no filler.

  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?

  For a destructive tool with no annotations and no output schema, the description covers the key facts: what is deleted, when to run it, and the default raw-file behavior. It could mention irreversibility or error cases, but it is largely complete for its simplicity.

  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%, so the description must compensate. It connects delete_raw to 'by default the raw log files' and connects ticket_id to 'the ticket's vectors'. This adds meaningful semantics beyond the bare 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 a specific action ('Clean up a ticket') and clearly identifies the resources being affected: embeddings and raw log files. This distinguishes it from sibling tools like ingest_ticket_logs, query_logs, and get_log_stats.

  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 gives an explicit temporal context: 'after the pipeline completes', and explains the intended benefit ('freeing disk and clearing stale data'). It does not name explicit alternatives or when-not-to-use cases, but the context is clear enough for an agent to decide.

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

• ingest_ticket_logsA
  4.4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden. It discloses the full processing pipeline (parse, chunk, embed, store), mentions token-budgeting behavior for Claude models, and notes that it returns ingestion stats. This is transparent about the action and side-effects (storing) without overpromising.

  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 two sentences: the first summarizes the core operation; the second provides input details and output. It is front-loaded with the purpose and contains no filler. Every sentence adds value.

  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?

  For a multi-step ingestion tool with no output schema or annotations, the description covers the necessary context: input source, processing behavior, and output type (stats). It does not detail return format or error cases, but those are not critical for tool selection, and the missing details are compensated by the clear pipeline description.

  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 coverage is 0%, so the description must compensate. It explains that 'ticket_id' is used to locate the default directory and that 'paths' is an optional explicit list override. This adds meaning beyond the raw schema and clarifies the role of each parameter.

  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 clearly states the action: 'Parse, semantically chunk, embed, and store all log files for a ticket.' It names the specific resource (log files) and the ticket scope. It also distinguishes from siblings by mentioning storage/ingestion, whereas siblings are query/delete operations.

  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 that it reads raw files from a default Jira MCP download location or an explicit 'paths' list, giving clear context on when to use it (after downloading logs). It does not explicitly state when not to use it or name alternatives, but the purpose is unambiguous and the context is sufficient.

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