snipara-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v2.8.24
Disambiguation5/5
Each tool has a clearly distinct purpose: ask, context query, search, read line range, get chunk, recall memory, remember if novel, end-of-task commit, inbox review queue/plan/apply, and stats. No overlapping functionality.
Naming Consistency5/5
All tools follow a consistent 'snipara_' prefix with snake_case descriptive names (e.g., snipara_context_query, snipara_end_of_task_commit). No mixing of conventions or abbreviations.
Tool Count5/5
12 tools is well-scoped for a documentation and memory management server. It covers viewing, searching, querying, memory operations, inbox review workflow, and statistics without being overwhelming or sparse.
Completeness4/5
The tool surface covers core retrieval, memory storage, and inbox review. Minor gaps include the lack of an explicit update or delete for memories, but the server appears focused on append and recall. Overall coverage is strong.
Average 4.6/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 commits in the last 12 weeks
- Last stable release on July 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that it is a 'read-only VIEWER operation' and 'changes no memory authority', and specifies return components (ranked memories, warnings, metadata, diagnostics). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences. The first sentence states the main purpose and scope. The second clarifies the read-only nature, directs to alternatives, and summarizes return structure. No wasted words.
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?
Despite 13 parameters and a nested object, the description omits details on return value structure and usage of complex parameters like 'correlation_context'. Given no output schema, more clarity on output format would improve completeness, but the description covers core purpose and behavior adequately.
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 does not add significant parameter-level detail beyond the schema; it only mentions return components. However, the schema itself is comprehensive, so additional explanation is not critical.
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 verb 'recall' and the resource 'durable Memory V2 decisions, learnings, preferences, and session carryover', and specifies the 'requested owner boundary'. It distinguishes from siblings by explicitly naming 'snipara_context_query' for source documents and 'snipara_memories' for lifecycle inventory.
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 states when to use this tool ('semantically recall durable Memory V2...') and provides alternatives ('Use snipara_context_query for source documents and snipara_memories for lifecycle inventory'), giving clear guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by stating it is a VIEWER operation with server-side synthesis and returns a bounded result, though it doesn't detail other behaviors like latency or auth requirements.
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?
Extremely concise: two sentences plus a comparison sentence, front-loaded with purpose and usage, with no superfluous content.
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?
Given the tool's simplicity, annotations, and lack of output schema, the description adequately covers purpose, usage, and return type. It could mention limitations on question complexity, but overall it's quite complete.
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 100%, so parameters are already described. The description adds overall context ('simple question') but no per-parameter details beyond the schema, meeting the baseline of 3.
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 tool answers a simple question from indexed project documentation with server-side synthesis, and distinguishes itself from siblings snipara_context_query and snipara_search, making the purpose very clear.
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?
Explicitly provides when to use this tool (simple question) and when to use alternatives (snipara_context_query for citations/controls, snipara_search for exact regex), offering excellent guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description clearly states it is an EDITOR operation that writes memory and workflow state, is non-idempotent, and that unknown fields fail closed. This adds significant behavioral context beyond annotations, which only provide hints about read-only, open-world, idempotent, and destructive nature.
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 concise at four sentences, front-loaded with the primary purpose, and every sentence adds value. No redundant or wasted text.
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?
Given the complexity of the tool (12 params, nested objects, no output schema), the description provides essential context: it explains return values, validation behavior, and alternative tools. It could be slightly more detailed, but overall it is adequately complete for an experienced agent.
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 has 100% description coverage for all 12 parameters, so the baseline is 3. The description adds minimal parameter-specific meaning beyond the schema, but the schema itself is comprehensive. No deduction needed.
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 tool persists end-of-task summaries, structured Why Capture, and durable outcomes for agent handoffs. It distinguishes from siblings by naming snipara_remember_if_novel for single reusable memories and document upload tools for source material.
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 explicitly mentions when to use snipara_remember_if_novel and document upload tools as alternatives. It also notes dry_run validates without writing, giving guidance on validation usage. However, it does not explicitly state when not to use this tool versus the named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark destructiveHint=true and idempotentHint=false. The description adds significant behavioral context: atomicity, authorization requirement with human admin identity, audit recording, 'fails closed' behavior, and return of per-item outcomes and a receipt. No contradiction exists.
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 concise, front-loading the core purpose in the first sentence. Each subsequent sentence adds distinct value (authorization, atomicity, failure behavior, return values). No wasted words. Structure is clear and scannable.
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?
Given no output schema, the description compensates by mentioning return values (per-item outcomes and receipt) and failure behavior (fails closed). It covers prerequisites, atomicity, and audit trails. Lacks detail on error scenarios or partial failure, but overall adequate for a destructive admin tool with rich schema and annotations.
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 100% with descriptive parameter definitions. The description does not add parameter-level meaning beyond what the schema already provides (e.g., review_plan_id, reason, actions). It reinforces overall context but does not enhance parameter understanding. 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 clearly states the tool applies memory-candidate and decision-draft actions from a prior review plan. It explicitly distinguishes from sibling tools like snipara_inbox_review_plan and snipara_inbox_review_queue by mentioning it must follow the plan and is the apply step. The use of 'atomic' and 'destructive ADMIN operation' further sharpens purpose.
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 clear prerequisites: must follow snipara_inbox_review_plan, requires explicit user authorization and a real human team-admin identity. It also notes it is not idempotent and fails closed if items changed. However, it does not explicitly state when not to use this tool versus alternatives, though the sibling set offers no other apply tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description reinforces annotations with 'read-only VIEWER operation and changes no project state.' It also discloses return contents: chunk text, source identity, position metadata, and bounded error result. This adds value beyond annotations by detailing output structure.
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?
Two sentences with no wasted words. First sentence states the core action. Second sentence provides usage guidance and a brief summary of return values. 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?
Despite no output schema, the description fully covers what the tool does, when to use it, what it returns, and how it differs from siblings. For a simple fetch-by-ID tool, this is complete.
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 100% with detailed descriptions for both parameters. Description adds minor context for chunk_id (its source) but does not significantly enhance understanding beyond the schema. 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?
Description states 'Fetch one complete indexed source chunk by the stable chunk ID returned from a reference-based context query.' It specifies the verb (Fetch), resource (chunk), and source of the ID. It also distinguishes the tool from snipara_read by noting its use for exact document line ranges.
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?
Description explicitly states when to use this tool: after snipara_context_query to fetch a chunk by ID. It also provides an alternative: use snipara_read for exact line ranges. This clearly guides the agent on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds useful behavioral context: it is an ADMIN operation, credential-like material is redacted, and it returns stable IDs, ownership, evidence, and review status. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each essential: purpose, behavioral transparency, and usage guidance. No wasted words; front-loaded with the core action.
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?
For a tool with one simple parameter and no output schema, the description fully covers what the tool does, what it returns, its security posture, and how it relates to sibling tools. No gaps.
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 single 'limit' parameter is already described in the input schema with default, max, min. The description adds no further parameter details. With 100% schema coverage, 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 specific verbs ('List'), identifies the resource ('human-reviewable memory candidates and ProjectDecision drafts'), and specifies binding context ('current user's team Dashboard Inbox'). It clearly differentiates from the sibling review_plan tool.
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?
Explicitly states it is a read-only ADMIN operation that changes no state, and directs the agent to use snipara_inbox_review_plan for validating recommendations before any authorized mutation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds that it is a 'VIEWER operation' that 'changes no project state,' returns 'capped matches,' and mentions validation/no-match result—adding context beyond 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?
Three sentences, front-loaded with purpose, then usage, then behavior. Every sentence adds essential information with no redundancy.
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?
No output schema, but description explains return type (capped matches with document and line context). Handles nested correlation_context as optional. With annotations for safety, description is fully sufficient for agent use.
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. Description adds minimal value: notes 'query' is an alias for 'pattern,' and mentions 'max_results' cap. Does not significantly enhance schema-provided meanings.
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?
Description states 'Search indexed project documentation with an exact regular expression,' specifying a clear verb, resource, and scope. It distinguishes from siblings snipara_context_query and snipara_read.
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?
Explicitly lists when to use (identifiers, paths, literal wording) and when to use alternatives (snipara_context_query for semantic, snipara_read after locating range). Provides return behavior (capped matches).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral details like timeout recovery, token budget behavior, and fallback to job queue. This goes beyond what annotations provide, though some redundancy exists.
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 primary purpose, then flows to usage, recovery, and alternatives. It is appropriately sized for a complex tool with 13 parameters, though some repetition in parameter details could be streamlined.
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?
Despite no output schema, the description explains return types (answer_pack, diagnostics, ranked sections) and covers timeout behavior, retry logic, token budget, and sibling tools. It addresses the complexity of nested parameters and provides sufficient context for an agent to use the tool effectively.
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 100%, so baseline is 3. The description adds extra meaning by contextualizing several parameters (query, max_tokens, search_mode, return_references, auto_decompose, include_all_tiers) within the timeout recovery scenario, enhancing their practical use beyond schema descriptions.
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 tool queries project documents, business files, and shared context for source truth and narrative documentation. It specifies the return type (answer_pack, diagnostics, ranked sections) and distinguishes from siblings by directing exact text searches to snipara_search and code queries to code-specific 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?
Explicit guidelines are provided: 'Use this first for source truth and narrative documentation.' A recovery strategy for timeouts is given in detail (narrow query, max_tokens 800-1500, etc.), and alternatives are named for exact text (snipara_search) and code context (snipara_code_* tools).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses behavioral traits beyond annotations: conditional storage based on novelty, duplicate skipping, auto_safe reconciliation superseding high-confidence matches without deletion, and potential state writes. Annotations provide idempotent and non-destructive hints, but the description adds nuanced behavior not captured in 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?
Concise, front-loaded with core purpose, and every sentence adds essential information. No fluff or repetition. Length is appropriate given the tool complexity.
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?
Despite 14 parameters and no output schema, the description is highly complete: it explains core logic (novelty, deduplication, reconciliation), scope semantics, return fields (storage status, duplicate evidence, memory ID, reconciliation actions), and references sibling tools. Covers all critical aspects for correct 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?
Schema description coverage is 100%, so baseline is 3. The description does not significantly add parameter-level meaning beyond the schema. While it provides context like 'never place secrets here' for document_refs, this is also in the schema. No major added value.
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 tool 'Store one durable Memory V2 record only when it is sufficiently different from existing reviewed memory.' It specifies it is an EDITOR operation, distinguishes from snipara_end_of_task_commit and context tools, and lists return values. The name and description together convey a precise verb-resource-action.
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?
Explicitly advises using snipara_end_of_task_commit for multi-outcome workflow capture and context tools for source documents, indicating when not to use this tool. It also describes duplicate call behavior and reconciliation modes, providing clear guidance on when the tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description reinforces 'without changing project state' and adds detail about opt-in sections and compactness. This adds value beyond annotations without contradicting them.
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?
Three sentences, each carrying distinct value: read-only purpose, opt-in details, alternative tool. Front-loaded with the core action. No redundant or unnecessary wording.
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?
For a simple tool with three optional parameters and no output schema, the description adequately explains what it returns (counts, token/index summaries, optional sections) and when to use the sibling tool. No critical gaps.
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 100% with each parameter having a description. The description adds context that file samples and index health are opt-in 'to keep the response small', which enhances understanding of the parameter defaults.
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 'Read compact project documentation and retrieval statistics', using a specific verb and resource. It distinguishes this tool from siblings by explicitly referencing snipara_index_health as an alternative for operational diagnosis.
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 snipara_index_health for operational diagnosis rather than this overview', providing clear when-not and alternative. This leaves no ambiguity about when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Confirms read-only nature and non-destructive behavior, aligning with annotations. Adds valuable details about snapshot creation, disposition handling (needs_human items remain pending), and return structure (immutable plan, exclusions, findings). No contradictions with 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?
Two sentences efficiently cover purpose, constraints, workflow, and output. Every clause earns its place with no fluff. Front-loaded with key action and constraints.
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?
While the description explains the overall workflow and mentions return of 'immutable plan, exclusions, and validation findings', it lacks a precise specification of the return format. However, given the simple input and no output schema, it is mostly complete. A minor gap in fully detailing the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage on all properties. The description adds context on how the recommendations parameter is used, particularly that needs_human items are excluded from the apply snapshot, enhancing understanding beyond 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 clearly states the tool validates evidence-backed recommendations and creates a snapshot-bound review plan. It distinguishes from siblings like snipara_inbox_review_queue and snipara_inbox_review_apply by specifying its role as a read-only planning step.
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?
Explicitly instructs to use snipara_inbox_review_queue first and never call apply without human authorization. This provides clear workflow context and when to use this tool vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description states it's a 'read-only VIEWER operation and changes no project state', which aligns with and adds context to annotations (readOnlyHint, idempotentHint, destructiveHint). Also specifies return behavior (resolved range and text, or errors).
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?
Three concise sentences with no waste. The first sentence states purpose, the second provides usage context, and the third describes output. Front-loaded and 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 moderate complexity (3 optional params, no output schema), the description covers purpose, usage context, parameter details, and return behavior (including errors). Annotations cover safety, and the description fills in behavioral details.
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 100%, so baseline is 3. Description adds value by explaining defaults (end_line defaults to start_line + 50), the relative/global nature of start_line, and accepted aliases for file_path (file, path, document).
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 'Read an exact line range from indexed project documentation', specifying the verb and resource. It distinguishes itself from siblings by mentioning alternatives like snipara_get_chunk and context after snipara_search or snipara_context_query.
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?
Provides explicit guidance: use after snipara_search or snipara_context_query when exact wording matters, and use snipara_get_chunk when you already have a cited chunk ID. This clearly indicates when to and when not to use the tool.
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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