retrace-mcp

by calvingunther66

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct query type: segment listing, app usage aggregation, tag listing, segments-by-tag, text search, and frame retrieval. The purposes are clearly differentiated, with no apparent overlap.
Naming Consistency3/5
Tool names follow a mix of conventions: list_* and get_* and search_text are verb_noun, while app_usage and segments_by_tag are noun phrases. This mixed pattern is still readable but lacks a single consistent style.
Tool Count5/5
Six tools is well within the ideal range for a focused query-only domain. Each tool serves a distinct analytical purpose without redundancy.
Completeness4/5
The surface covers the core query needs: segment listing, app usage, tag filtering, text search, and frame detail. Minor gaps exist, such as no dedicated get_segment tool or tag management, but these are not critical for the likely read-only intent.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- 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.
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}
```
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about how the data is computed ('from summed segment durations') and states the limit bounds. However, it does not disclose return format, pagination, or behavior when no data exists, so it does not go far 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?
The description is compact, front-loaded with the core purpose, and includes a clearly formatted Args block. Every sentence contributes necessary information without redundancy or fluff.
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, the presence of an output schema, and strong annotations, the description covers the essential inputs and purpose. It does not explain when to use it vs siblings, but for a straightforward query tool that may be acceptable. The description is sufficiently complete for an agent to invoke it 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%, so the description bears the full burden of explaining parameters. It does this well: start and end are defined as 'ISO 8601 local start/end of the window', and limit is specified as '1-100, default 20'. The only minor issue is that the schema lists limit's default as null while the description says 20, a slight inconsistency in default value specification.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports 'Time spent per app over a window', specifying both the resource (apps) and the scope (time window). It does not explicitly use a verb like 'get' or 'list', but the intent is unambiguous. It does not differentiate from sibling tools, but its focus on app usage is distinct enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any context or exclusions, such as 'use this for aggregated usage, use list_segments for raw segments'. The only implied context is a time window, but no explicit usage scenario is described.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds parameter semantics and the definition of a segment, but does not disclose return format, ordering, or pagination behavior beyond the limit parameter. 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?
One-sentence purpose plus a compact Args block. Every line is informative, no redundancy, well-structured.
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?
With an output schema present and all parameters explained, the description is essentially sufficient for a read-only list operation. It slightly lacks comparative usage context with sibling tools, but that is not required for functional completeness.
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?
The input schema has 0% description coverage, but the description fully explains all four parameters: start/end as ISO 8601 bounds on segment start, bundle_id as a filter, and limit with range/default. This fully compensates for the schema gap.
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 clear verb+resource statement ('List capture segments') and defines the domain concept in parentheses, distinguishing it from sibling tools that list tags or search text. This is a specific, unambiguous purpose.
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?
There is no explicit guidance on when to use this tool vs alternatives like segments_by_tag. The parameter descriptions imply it can be filtered by app, but no alternative/exclusion criteria are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds FTS5 query syntax and parameter defaults, but no behavioral traits such as pagination or return format. It does not contradict annotations, and the bar is lower due to rich annotation coverage.
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 structured with 'Args:' and uses clear, bullet-like lines. It is slightly verbose for the purpose but every sentence adds value, especially the query examples. No wasted words.
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 that an output schema exists, return values do not need to be described. The description covers all parameters and uses the FTS5 syntax detail effectively. It is complete for a search tool with good annotations and an output schema.
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?
Schema description coverage is 0%, so the description fully compensates. It explains each parameter: query with concrete FTS5 examples, start/end as ISO 8601, bundle_ids as allow-list, and limit range (1-100, default 20). This goes far beyond the raw 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 'Full-text search across OCR'd screen content,' using a specific verb and resource. This distinguishes it from siblings like list_segments or get_frame, which target different aspects.
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 context (searching screen content) but does not explicitly mention when to use this tool instead of alternatives. No exclusions or alternative names are provided, so context is clear but not comparative.
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 signal read-only, idempotent, and non-destructive behavior. The description adds a non-obvious output convention: coordinates are normalized fractions (0.0–1.0) rather than pixels, and it clarifies the limit semantics. It does not contradict the annotations, so the disclosure is solid.
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 front-loaded with a one-sentence purpose, followed by a necessary coordinate-normalization caveat and a compact Args block. No filler or redundant repetition of schema fields.
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 two-parameter frame fetch with an output schema and strong annotations, the description covers the key semantics: what is returned, the coordinate system, and limit constraints. It does not need to explain return structure because the output schema exists, and no missing usage context undermines its completeness.
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?
With 0% schema description coverage, the description compensates fully: frame_id is explained as a numeric id, and limit is given a range (1-100) and default (20) beyond the schema's plain integer/null type. The only minor issue is that the schema specifies default null while the description says 20, but the added guidance is substantial.
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 'Fetch one frame with its OCR text boxes and parent segment context,' naming a specific verb (fetch), resource (frame), and payload. This clearly distinguishes it from list/search siblings like list_segments, segments_by_tag, and search_text. It is not a tautology or vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit statement of when to use this tool versus alternatives such as list_segments or search_text. The description implies 'fetch a single frame,' but it does not state prerequisites, fallback cases, or when not to use it. This is the weakest dimension.
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 read-only, idempotent, and non-destructive behavior. The description adds important context beyond annotations: counts are for 'visible' segments only, and the limit range/default is specified. This clarifies scope and constraints that annotations do not convey.
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 minimal and front-loaded: a single clear sentence stating the tool's purpose, followed by a compact parameter specification. Every sentence adds value 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?
For a simple tool with one optional parameter and existing annotations plus an output schema, the description covers all necessary aspects: what it does, the visibility scope, and parameter constraints. It is fully adequate for agent invocation.
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?
Schema description coverage is 0%, but the description fully compensates: it documents the limit parameter's range (1-100) and default (20), including the actual default which differs from the schema's null. This is essential for correct invocation.
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 lists segment tags and counts visible segments per tag. It distinguishes from siblings like list_segments (which likely lists segments) and segments_by_tag (which likely lists segments for a specific tag), as it focuses on tag-level aggregate counts.
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 use case (viewing tag distribution with visible segment counts) but does not explicitly state when to prefer this over alternatives or exclude other scenarios. No sibling comparison is provided, so guidance is left to inference from the verb and resource.
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 indicate a safe read-only, idempotent operation. The description adds that tag matching is exact and that limit is a 1-100 range with a default of 20, which are useful behavioral details beyond the schema.
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 extremely compact, leading with a one-sentence purpose and followed by a minimal Args list. No extraneous information.
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 only 2 parameters and the presence of an output schema, the description covers the purpose, parameter semantics, and prerequisite workflow. It lacks explicit guidance on when to use alternatives, but is otherwise sufficient for a simple filtered read tool.
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 has no property descriptions (0% coverage). The description explains that tag must be an exact value from list_tags and clarifies limit's range/default. However, the stated default of 20 conflicts with the schema's default of null, which could confuse an agent.
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 'List segments carrying a given tag name,' which clearly identifies the verb (List), resource (segments), and scope (by tag). It differentiates from sibling tools like list_segments (unfiltered) and list_tags (tags only).
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 instructs that the tag must be an exact name as returned by list_tags, establishing a clear prerequisite and workflow. It also specifies the limit range, but does not explicitly contrast with list_segments or provide exclusion criteria.
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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