Universal AI-driven comparison engine: ask in natural language, get a real-time comparison built from real, sourced data. Provably neutral (never pay-to-rank), traceable sources, multi-language and multi-country. Built for both humans and AI agents (MCP, JSON, Markdown).
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.2/5 across 3 of 3 tools scored.
Each tool serves a distinct purpose: search to find comparisons, get to retrieve details, and list categories for browsing. No overlap.
All tools use consistent verb_noun snake_case naming: get_comparison, list_categories, search_comparisons.
Three tools is minimal but adequate for a read-only comparison catalog. Slightly thin but still reasonable.
Covers search, retrieval, and category listing, but missing a way to list all comparisons within a category without a specific search query.
get_comparisonAInspectFetch one full structured comparison (items with scores, sub-scores, per-criterion values, badges, sources, sourced/estimate flags, freshness). Neutral ranking, never paid. Data is localized to the requested lang/country.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | 2-letter language code. Default: en. | |
| slug | Yes | Comparison slug (from search_comparisons). | |
| country | No | 2-letter country code. Default: US. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses data freshness, sourcing flags, localization, and impartiality ('neutral ranking, never paid'). Does not discuss errors or permissions, but adequate for a read-only fetch.
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, front-loaded with the key action and result, and the second sentence provides critical behavioral context. 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 fetch tool with 3 parameters and no output schema, the description covers what is returned, neutrality, and localization. Minor gap: no explicit mention of error behavior, but not critical.
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%, and description adds 'localization' context but no additional parameter meaning beyond the schema's own descriptions. Baseline 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 fetches a 'full structured comparison' with specific components listed, and distinguishes it from siblings by emphasizing it retrieves a single comparison by slug.
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 implicitly suggests use after obtaining a slug (from search_comparisons), but lacks explicit when-to-use or when-not-to-use guidance. The neutral ranking and localization hint at appropriate contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspectList the top-level categories available for a market, with the number of comparisons in each.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | 2-letter language code. Default: en. | |
| country | No | 2-letter country code. Default: US. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral burden. It states the tool lists categories and includes comparison counts, which is transparent for a read operation. However, it does not explain how lang/country affect results or any potential error states.
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, clear sentence with no filler. It is concise and front-loaded with the action and context.
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 that there is no output schema, the description adequately conveys the return value (categories with comparison counts). It could mention whether categories have identifiers or if results are paginated, but for a simple list, it is complete enough.
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 descriptions for both parameters. The description adds no additional meaning beyond the schema, which is adequate but not enhanced.
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 action ('List'), the resource ('top-level categories'), and the context ('for a market', 'with the number of comparisons'). It distinguishes from siblings (get_comparison, search_comparisons) by focusing on categories rather than comparisons.
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 the tool (to list categories), but does not explicitly specify when not to use it or how it differs from alternatives. For a simple listing tool, this is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_comparisonsAInspectSearch CompareFairly's catalog of neutral, sourced comparisons for a given market. Returns matching comparisons (title, slug, url, json_url, popularity). Use this first to find the exact slug, then call get_comparison.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | 2-letter language code (e.g. en, fr, de, es, it). Default: en. | |
| limit | No | Max results (default 20, max 50). | |
| query | No | Free-text query, e.g. 'best electric car' or 'password manager'. | |
| country | No | 2-letter country code (e.g. US, FR, DE, GB). Default: US. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses it searches a catalog, returns specific fields (title, slug, etc.), and is neutral/sourced. Lacks details on pagination, ordering, or data freshness, but adequate for a read-only search tool.
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?
Extremely concise: two sentences plus return fields list. Front-loaded with verb and resource. 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 4-parameter tool without output schema, the description is fairly complete: explains purpose, workflow, return fields. Could mention ordering or default limit behavior, but covers core aspects.
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% and all parameters are documented. The tool description adds workflow context but does not significantly enhance meaning beyond the schema's parameter descriptions.
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 the verb 'search' and resource 'catalog of neutral, sourced comparisons'. Distinguishes from sibling 'get_comparison' by mentioning the workflow: find slug first, then call get_comparison.
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 says 'Use this first to find the exact slug, then call get_comparison', providing clear when-to-use and when-not-to-use guidance. Implicitly differentiates from list_categories by focusing on search behavior.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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