Consensus 'best X for Y' rankings from ChatGPT, Claude, Gemini & Grok. Search, verdicts, history.
Average 4.2/5 across 5 of 5 tools scored.
Each tool targets a distinct operation: listing categories, searching, getting a category leaderboard, polling history, or product details. There is no functional overlap, ensuring an agent can clearly differentiate between them.
All tools follow a consistent verb_noun pattern (get_best_in_category, get_poll_history, get_product, list_categories, search_best), with clear verbs and descriptive nouns, promoting predictable usage.
With 5 tools, the server is well-scoped for a domain focused on ranking retrieval and exploration. Each tool serves a clear purpose without unnecessary bloat or inadequacy.
The tool set covers the full workflow: discover categories (list_categories), search for best (search_best), view detailed rankings (get_best_in_category), audit trail (get_poll_history), and cross-category product info (get_product). There are no obvious gaps for the intended use case.
get_best_in_categoryGet a category's full rankingAGet the full ranked leaderboard and verdict for a category slug (obtained from search_best or list_categories), e.g. 'best-llm-observability'.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Category slug, e.g. best-llm-observability |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and open-world. Description adds that output includes 'leaderboard and verdict', but no details on behavior like pagination or rate limits. Sufficient given annotations.
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?
Single sentence, front-loaded with action, no superfluous words. Highly concise.
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?
Simple tool with one param and no output schema. Description covers input source, output nature, and example. Complete for agent usage.
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 covers parameter fully. Description adds context: slug comes from specific sibling tools, provides example. Adds value beyond schema.
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?
Clear action: get full ranked leaderboard and verdict. Specific resource: category slug. Differentiates from siblings by specifying source of slug (search_best or list_categories).
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?
Explicitly states slug is obtained from sibling tools, indicating proper usage context. No exclusions or alternatives listed, but clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_poll_historyGet a category's poll audit trailAGet the raw poll history — every model's pick over time — behind a category's verdict. The credibility/audit trail. Optionally filter by model.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Category slug, e.g. best-llm-observability | |
| limit | No | Optional: max rows (default 100). | |
| model | No | Optional: only this model's picks. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds context such as 'raw poll history' and 'credibility/audit trail', clarifying the nature of the data retrieved (historical, per-model picks) beyond the annotations.
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 two sentences, front-loaded with the main action. It is concise with no unnecessary words, though the second sentence is somewhat redundant.
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 description lacks details about the return value structure (e.g., format of picks, timestamps). With no output schema, it should provide more behavioral context. It is moderately complete but leaves gaps.
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 coverage is 100% with all parameters described. The description mentions optional model filtering but adds no new detail beyond the schema. Baseline score of 3 is appropriate.
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 retrieves raw poll history, specified as 'every model's pick over time behind a category's verdict'. It distinguishes from siblings like 'get_best_in_category' by focusing on the audit trail rather than the current best.
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?
The description implies usage for historical audit data but does not explicitly state when to use this tool over alternatives like 'get_best_in_category' or 'list_categories'. No exclusions or when-not-to-use guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_productGet a brand's recordAGet a single brand/product's record across every category it is ranked in, with verdicts and its homepage.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug, e.g. langfuse |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral context about the return data structure (count, category rankings, verdicts, homepage), which goes 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single, well-structured sentence with no redundancy. It front-loads the core action and key details efficiently.
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?
For a simple tool with one parameter and no output schema, the description sufficiently explains what the tool returns and its scope, fully covering necessary context.
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% for the single slug parameter. The description does not add extra meaning beyond what the schema provides (e.g., example value). Baseline 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 it gets a single brand/product's record across all ranked categories, including verdicts and homepage, distinguishing it from siblings like search_best or get_best_in_category.
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?
The description implies when to use (when needing a product's full record), but does not explicitly differentiate from siblings or provide when-not-to-use guidance. It relies on the agent inferring usage from purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesList all categoriesAList every category ModelsAgree ranks (slug + title). Large; prefer search_best when you have a specific need.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and openWorld; description adds 'Large' warning about output size, which is useful 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words, front-loaded with purpose and use guidance.
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?
For a zero-parameter tool with no output schema, description fully covers what it returns and when to use alternatives.
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?
No parameters, baseline score 4 as per guidelines; description doesn't need to add parameter info.
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?
Clearly states it lists every category with slug and title, and distinguishes from sibling search_best.
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?
Explicitly advises to prefer search_best when having a specific need, providing clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_bestSearch best-X rankingsAFind what AI models agree is the best product/tool/service for a need, or look up how a specific brand ranks. Returns matching categories and brands with a dated one-sentence verdict and source URLs. Use for any 'best X for Y' question. Excludes medical, financial, and legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A category, use-case, or brand name — e.g. 'best llm observability', 'ci/cd for cloud native', or 'Langfuse'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral details: returns categories, brands, dated verdict, and source URLs. This goes beyond annotations, though it doesn't cover pagination or rate limits.
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 four sentences, front-loaded with the core action. Every sentence adds value: purpose, return format, usage guidance, and exclusions. No wasted words.
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?
For a simple read-only tool with one parameter and no output schema, the description is complete. It explains input, output format, use cases, and exclusions. Sufficient for an agent to select and invoke correctly.
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?
With 100% schema coverage, the schema already describes the single parameter with examples. The description adds no new semantic detail beyond what is in the schema, so it meets the 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 clearly states the tool finds consensus rankings from AI models and looks up brand rankings. It specifies the input as a category, use-case, or brand name. However, it does not explicitly differentiate from sibling tools like get_best_in_category, missing a chance to clarify boundaries.
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?
The description explicitly says 'Use for any best X for Y question' and excludes medical, financial, and legal advice. It provides clear when-to-use guidance but does not mention alternative tools for more specific queries.
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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Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Claim ownership of the server listing
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