synapse-research

Server Quality Checklist

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Latest release: v0.3.0
Disambiguation4/5
Tools are mostly distinct: get, list, search, delete, start for research runs, plus add_field and rebuild_index for supporting operations. However, start_research and the list/search tools could cause some confusion, as start_research creates a run while list/search find existing ones.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_research_run, add_field). No mixing of conventions or vague verbs.
Tool Count5/5
With 7 tools, the server is well-scoped for managing research runs and associated schema/index operations. Not too few or too many relative to the domain.
Completeness3/5
The server covers core operations for research runs (create via start_research, read via get/list/search, delete) but lacks an explicit update operation for runs. Schema management is limited to adding fields. This is a notable gap.
Average 4/5 across 7 of 7 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 3 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 27, 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.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It does not disclose whether the tool is read-only, requires authentication, or has rate limits. The only behavioral hint is the ID format.
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 very concise with no wasted words. However, it could include additional context without being verbose, such as mentioning the tool is for single-record retrieval.
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 presence of an output schema and a simple single-parameter input, the description is minimally adequate. It doesn't mention error handling or that it returns a single run, but the context is sufficient for basic use.
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 description adds value beyond the schema by noting that IDs start with 'rr_'. The schema already provides a detailed description, but the ID format note further clarifies parameter semantics.
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 it gets a research run by ID, which is unambiguous. However, it does not explicitly differentiate from sibling tools like delete_research_run that also operate by ID, though the verb 'get' implies retrieval.
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?
No guidance on when to use this tool versus siblings like list_research_runs or search_research_runs. The description lacks context for appropriate use cases or prerequisites.
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?
No annotations are provided, so the description must carry the full burden. It indicates mutation by mentioning writing to disk and reloading, and validation for safety. However, it omits details on error handling, permissions, or other side effects.
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, no redundant information, and immediately states the core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters and no parameter descriptions, the tool is incomplete for an agent to use correctly. The output schema exists but is not shown; the description covers high-level behavior but lacks crucial input semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not explain any of the 6 parameters. The agent receives no guidance on the meaning or usage of fields like 'default', 'required', 'field_name', etc.
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 ('Add a new field to an entity schema') and the resource ('entity schema'). The sibling tools are all about research runs, so this tool is distinct.
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 includes what happens (validate, write, reload) but does not provide explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. States ordering and ID prefix, but does not mention read-only nature, auth requirements, rate limits, or empty result behavior.
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 detail. Front-loaded with purpose, then adds key behaviors. No superfluous text.
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?
Covers main filters and ordering, but misses explaining '_name' parameter, limit behavior (max? paginated?), and read-only safety. Output schema exists, so return format is covered, but gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only 'status' is partially described as filter with default. No explanation for '_name' (default 'research_run') or 'limit' (default 50). Schema coverage is 0%, so description adds minimal value beyond 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?
Clearly states it lists research runs with filtering by status and ordering. Distinct from siblings like get_research_run (single), search_research_runs (search), and delete_research_run (delete).
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?
Implied usage via filters and ordering (use when you need a filtered, ordered list), but no explicit when-not or alternatives mentioned.
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?
No annotations are provided, so the description carries the full burden. It mentions 'Force a full rebuild' implying it is potentially heavy, but lacks details on side effects, permissions, or safety considerations. Adequate but not thorough.
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 convey the action and usage context. No extraneous information; front-loaded with the purpose.
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 the tool has no parameters and a clear purpose, the description sufficiently covers what the tool does and when to use it. An output schema exists but the description does not need to detail return values for a rebuild operation.
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?
There are zero parameters, so the description does not need to explain param semantics. The description adds context about the use case, meeting the baseline for no-parameter tools.
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 'rebuild' and the resource 'relationship index', with specificity about the source ('from entity files'). It is distinct from sibling tools which focus on research runs.
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?
Provides explicit conditions for use: 'if the index seems stale or after manual file edits.' While it doesn't mention when not to use or alternatives, the tool is simple and such guidance is implied.
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?
No annotations are provided, so description carries full burden. It discloses the default soft delete behavior (status set to 'deleted'), the hard permanent delete option, and the ID prefix. It does not mention required permissions, rate limits, or cascading effects, but the core behavioral traits are well covered.
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?
Description is three concise sentences with no redundancy. It front-loads the core purpose and then adds behavioral nuances efficiently.
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 simplicity (2 params, no nested objects) and presence of an output schema, the description covers all necessary behavioral details: soft vs hard delete, default behavior, and ID format. It is adequately complete for an agent to invoke 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 coverage is 100%, so baseline is 3. Description adds value by clarifying the 'hard' parameter's effect ('to permanently remove') and reiterating the ID prefix. This enriches the parameter meaning beyond schema alone.
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 clearly states 'Delete a research_run by ID', specifying both the action and resource. It also explains soft vs hard delete. Sibling tools are all non-destructive (get, list, search) or other actions (add_field, rebuild, start), so this tool's deletion purpose is distinct and 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?
Description implicitly indicates when to use (to delete a research_run) and explains default soft delete behavior. It does not explicitly exclude use cases or mention alternatives, but given sibling tools are read or other operations, 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. Discloses case-insensitive search across all string fields, filter operators, sort syntax, and ID prefix ('rr_'). Lacks explicit read-only statement but implies safe operation.
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, front-loaded with purpose. Every word adds value. No 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 no annotations and presence of output schema, description is fairly complete: covers search behavior, filters, sorting, and default _name. Mentions ID prefix for identification. Slight gap in explaining lack of required parameters.
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 coverage is 0%, yet description adds meaning for all 5 parameters: query (free text), filter (object with operators), sort (descending with '-'), limit (default 20), _name (default 'research_run'). Compensates fully.
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 clearly states the tool searches research_runs with text query and/or structured filters. It distinguishes itself from siblings like get_research_run (single), list_research_runs (likely unfiltered), and delete_research_run.
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?
Explains when to use text query vs structured filters, and mentions sorting with '-field' for descending. Does not explicitly exclude alternatives, but context is clear enough for an agent to differentiate.
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?
With no annotations provided, the description fully covers behavioral traits: the tool creates a 'research_run' entity, updates progress in real time, typical execution time (60s to 3 min), and return format (markdown report). It also explains the side effects of the 'title' parameter and the fallback logic for seed parameters.
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 fairly long but front-loaded with the main purpose. Every sentence adds value given the tool's complexity, though it could be slightly more concise by combining some details. Still well-structured with clear separation of topics.
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 (async, two modes, multiple parameters with fallback logic) and the existence of an output schema, the description provides complete context. It covers all necessary details for an agent to correctly select and invoke the tool, including practical tips and error scenarios.
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?
Although schema coverage is 100%, the description adds significant meaning beyond the schema. It explains why 'title' is optional (saves a background LLM call), the trade-offs between 'seed_uri' and 'seed_data' (resource extension vs universal fallback), and the error handling for 'seed_uri' when the host doesn't support the extension.
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 'Run' and the resource 'research task on the given query'. It distinguishes itself from sibling tools (get/list/search/delete) by being the only tool that initiates research. The explanation of both blocking and task augmentation modes further clarifies its purpose.
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 provides explicit guidance on when to use the tool, including two execution modes (MCP task augmentation vs blocking), advice on using the 'title' parameter to save a background LLM call, and clear instructions for choosing between 'seed_uri' and 'seed_data' based on host capabilities. It also tells the agent to retry with 'seed_data' if 'seed_uri' fails.
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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