Synthesize GitHub Issues, HN and App Store reviews into ranked pain clusters. Pay-per-call x402.
Quick single-source pain point extraction.
| Name | Required | Description | Default |
|---|---|---|---|
| top_n | No | Number of top pain points to return (default 10). | |
| source | Yes | Source config with "type" (github_issues, hackernews, appstore) and "target". | |
| max_items | No | Max feedback items to analyze (default 50). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only mentions 'quick' and 'single-source' without addressing side effects, permissions, read-only nature, rate limits, or any other runtime behavior. This 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that directly states the tool's purpose without unnecessary fluff. It is well-structured and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (nested source object, parameters with defaults) and lack of usage guidance, the description is too minimal. It does not explain what 'pain points' means, how they are determined, or when to use this tool, leaving the agent with insufficient context despite having an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions cover 100% of parameters, each with a clear meaning (source type/target, top_n count, max_items count). The tool description adds no extra insights beyond the schema, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts pain points from a single source, distinguishing it from sibling tools like sentiment trends or search. The verb 'extraction' and object 'pain points' are specific, and the 'single-source' qualifier adds clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The term 'single-source' hints at a limitation, but there is no direct statement about when to prefer this over search_feedback or synthesize_feedback.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Time-series sentiment analysis across feedback sources.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Only include feedback after this date (ISO 8601). | |
| sources | Yes | List of source configs. Each must have "type" and "target". | |
| granularity | No | Time granularity — "weekly" or "monthly" (default "weekly"). | weekly |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It doesn't disclose whether the tool is read-only, whether there are side effects, or any limitations. The description is purely functional with no behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but under-specified. It's not verbose, but it lacks detail needed for effective use. It's appropriately front-loaded but a bit too sparse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even with an output schema present, the description doesn't explain return types or formatting. It provides no context about the structure of the time-series output or how to interpret results. For a tool with three parameters and significant output, the description is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes all parameters with 100% coverage, so the baseline is 3. The description adds no additional meaning beyond what the schema provides, so it stays at baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action (time-series sentiment analysis) and resource (feedback sources), but it doesn't distinguish itself from sibling tools like get_pain_points or search_feedback. It's clear enough but lacks differentiation, making it average.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-text search across cached feedback items.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default 20). | |
| query | Yes | Search query string. | |
| since | No | Only include items after this date (ISO 8601, e.g. "2025-01-01"). | |
| source | No | Singular alias for sources — accepts a single source type string. If both source and sources are provided, sources takes precedence. | |
| target | No | Optional target filter (e.g. "owner/repo" or app ID). | |
| sources | No | Optional list of source types to filter (e.g. ["github_issues"], ["github"]). Accepted values: github_issues (or "github"), hackernews, appstore. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral transparency burden. It only discloses that the data is 'cached' but omits details such as result ordering, pagination behavior, filtering semantics, and data freshness. This is minimal for a search tool with six parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It communicates the core purpose efficiently while leaving detailed parameter semantics to the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The schema and output schema cover most operational details, and the description provides the essential purpose and the 'cached' caveat. However, the lack of when-to-use guidance and behavioral nuance makes it only minimally complete for a six-parameter search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3; the schema thoroughly documents each parameter, including the source/sources precedence. The description itself adds no parameter-level meaning beyond the generic 'full-text search' phrase.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies a specific action ('Full-text search') and resource ('cached feedback items'), making its purpose immediately understandable. It distinguishes itself from sibling tools focused on pain points, sentiment trends, and synthesis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the 'full-text search' phrasing and sibling tool names, but no explicit guidance is given for when to choose this over alternatives. It also does not mention exclusions or when a different tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Multi-source feedback synthesis into ranked pain clusters.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | Optional focus area to prioritize (e.g. "performance", "onboarding"). | |
| since | No | Only include feedback after this date (ISO 8601, e.g. "2025-01-01"). | |
| sources | Yes | List of source configs. Each must have "type" (github_issues, hackernews, appstore) and "target" (e.g. "owner/repo", "product name", app ID). | |
| max_items_per_source | No | Max feedback items per source (default 100). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It only states 'synthesis' but does not clarify if it's read-only, how sources are merged, error handling, or any side effects. The tool appears to analyze input and return results, but the lack of any operational details leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is clear and immediately front-loads the primary purpose. Every word is necessary; there's no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the schema and output schema provide structural details, the description lacks guidance on edge cases, input constraints, and use-case alignment. It does not explain how the synthesis works or any limitations, making it incomplete for an AI agent to confidently invoke.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema—it doesn't elaborate on parameters like 'focus' or 'since' or how to configure sources; it only repeats the notion of 'multi-source'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Multi-source feedback synthesis into ranked pain clusters' clearly states the action (synthesis) and the resource (multi-source feedback) with a specific output format (ranked pain clusters). This differentiates it from sibling tools like get_pain_points (which likely retrieves existing clusters) and search_feedback (which is a search operation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention intended scenarios, prerequisites, or explicit alternatives, leaving the agent to infer from the tool name and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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The tools are mostly distinct: pain point extraction vs. sentiment trend analysis vs. full-text search vs. synthesis. However, 'get_pain_points' and 'synthesize_feedback' could overlap somewhat in outcome (pain points vs. clusters), though the descriptions clarify different purposes.
Tools follow a 'verb_noun' pattern with 'get_' and 'search_' and 'synthesize_' verbs. The inconsistency: 'get_pain_points' and 'get_sentiment_trends' are 'get' but 'synthesize_feedback' uses a different verb. Naming is clear but not perfectly uniform.
Four tools for a feedback synthesis server is well-scoped. Each tool covers a necessary function without excess.
The tool surface covers key extraction, trend, search, and synthesis operations, but lacks obvious management functions like adding/updating feedback sources or caching, and no explicit export or reporting tool. Minor gaps exist but the core purpose is covered.
