Search, rank, and compare 500,000+ AI models, datasets, papers from 13+ platforms. Hardware-aware model selection with VRAM and license constraints. 5 tools: search, rank, explain, select_model, compare.
Scored across 5 tools
Significant overlap exists between free2aitools_search, free2aitools_rank, and free2aitools_select_model. All return FNI-ranked results with largely similar functionality; the descriptions attempt to differentiate but boundaries remain unclear. Compare and explain are distinct, but the discovery tools cause confusion.
All tools share the 'free2aitools_' prefix and lowercase snake_case, but four use single verbs (compare, explain, rank, search) while one uses 'select_model' (verb_noun), creating a minor inconsistency. Overall naming is predictable and readable.
With 5 tools covering discovery, explanation, and comparison of AI models, the count is well-scoped for the server's purpose. Each tool has a defined role, and the set is neither too sparse nor overwhelming.
The tools cover key workflows: keyword search, metadata filtering, ranking, single-entity explanation, and multi-entity comparison. A minor gap is the absence of a tool to retrieve full details of a specific entity without explanation, but this can be approximated. Overall, the surface is largely complete for discovery and analysis.
Compare 2-25 AI catalog entities side-by-side — any catalog entity type (models, datasets, papers, tools), not models only — showing FNI scores, factor breakdown (Semantic, Authority, Popularity, Recency, Quality), specs (params, VRAM, context length) where applicable, and license. USE WHEN you already have 2+ specific entity ids and want a structured side-by-side. DO NOT USE to discover entities, to run/execute a model, or to get a recommendation; the tool presents comparison facts for the caller to decide on, is not an inference router, and returns no paid placement. Read-only, no side effects, no billing. Cold upper-range multi-paper requests may return a transient 503 (retry after the indicated delay). Use free2aitools_select_model or free2aitools_search to discover candidates first, then compare the top ones.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | Catalog entity IDs to compare (2-25), any entity type. Use the id from search/rank/select_model results verbatim (e.g. ["hf-model--meta-llama--llama-3-8b", "arxiv--2401.00001"]) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden. It states the tool is read-only with no side effects or billing, notes a potential transient 503 error for cold multi-paper requests with retry guidance, and clarifies it is not an inference router and returns no paid placement. These details exceed typical behavioral disclosure.
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 well-structured: core action first, then usage conditions, then behavioral details, and finally a pointer to sibling tools. Every sentence adds value without redundancy. It is appropriately sized for the complexity.
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?
Despite no output schema, the description lists what the comparison shows (FNI scores, factor breakdown, specs, license) and mentions error conditions (503). It lacks a description of the output format but is sufficiently complete for an agent to understand the tool's capabilities and limitations.
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 baseline is 3. The tool description does not add further meaning beyond what the input schema already provides (e.g., id format, count range). The description repeats the schema's example but adds no new semantic detail.
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's purpose: compare 2-25 AI catalog entities side-by-side, listing what it shows (FNI scores, factor breakdown, specs, license). It distinguishes from siblings by explicitly noting that this tool is for comparison and suggests using free2aitools_select_model or free2aitools_search for discovery.
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 provides explicit when-to-use and when-not-to-use guidance: 'USE WHEN you already have 2+ specific entity ids and want a structured side-by-side. DO NOT USE to discover entities, to run/execute a model, or to get a recommendation.' It also names sibling tools as alternatives for discovery.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain why one specific entity received its FNI score, returning the 5-factor breakdown: Semantic (S), Authority (A), Popularity (P), Recency (R), Quality (Q). FNI = 0.35S + 0.25A + 0.15P + 0.15R + 0.10*Q (the S factor is a baseline, surfaced with a caveat, not a measured per-entity value). USE WHEN you already have one entity id (from a search/rank/select result) and want its score rationale. DO NOT USE to search/discover entities, to run a model, or to get a recommendation — this only describes scoring evidence for the caller to interpret. Read-only, no side effects, no billing. Use free2aitools_compare instead for side-by-side differences across multiple entities.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Entity name or ID to explain (e.g. "Llama-3", "hf-model--meta-llama--llama-3-8b") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully covers behavioral traits: it states 'Read-only, no side effects, no billing' and clarifies the caveat about the S factor (not a measured per-entity value).
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?
Every sentence serves a clear purpose, explaining function, usage, limitations, and formula. It is slightly verbose but well-structured and front-loaded with the core action. 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?
Given the complexity of the scoring formula and the absence of an output schema, the description completely covers all aspects: purpose, usage, behavioral traits, parameter details, formula breakdown, and caveats. It leaves no ambiguity.
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% for the single required parameter 'id'. The description adds meaning by providing example values like 'Llama-3' and specifying the format 'hf-model--meta-llama--llama-3-8b'.
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 explains why an entity received its FNI score with a 5-factor breakdown, and includes the formula. It distinguishes itself from sibling tools like free2aitools_compare by specifying its unique purpose.
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?
Explicit usage guidance: 'USE WHEN you already have one entity id...' and 'DO NOT USE to search/discover entities...'. It also names an alternative sibling (free2aitools_compare) for side-by-side comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword-search AI entities using the task/query text as input and return matching catalog entries. Search results are ordered by a relevance score based on the FNI and, where term-match data is available, how well the entry matches the query. The score used for ordering may differ from the fni_score field returned in the response. The result set is bounded. Mechanically this is the same keyword search as free2aitools_search with the task text folded into the query; it does NOT perform task-fit recommendation, compatibility analysis, model inference, or model execution, and it is NOT an inference router. USE WHEN you have task text and want catalog entries ordered by that relevance score. The caller makes the final selection; results are never paid placement and there is no billing. Read-only, no side effects. May return a retryable transient 503 under cold-path or fallback budget limits; retry according to Retry-After. Use free2aitools_search for plain keyword discovery, or free2aitools_select_model to apply hardware/license metadata filters.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | Optional task context to combine with query for more targeted ranking | |
| limit | No | Max results to return (1-20, default 10) | |
| query | Yes | Search query describing what to rank (e.g. "text generation", "object detection") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure — and it delivers richly. It states 'Read-only, no side effects,' discloses that results are never paid placement and there is no billing, warns that the ordering score may differ from the fni_score field returned, notes the result set is bounded, and documents a retryable transient 503 with retry guidance according to Retry-After. This goes well beyond a minimal safety disclosure.
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 dense and every sentence earns its place — disambiguation, safety profile, retry behavior, and alternative routing are all necessary context. It is appropriately front-loaded with the core purpose statement. It is on the long side, but given how many wrong assumptions it prevents (paid placement, inference routing, equivalent to search), the length is justified rather than wasteful.
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 there is no output schema, the description still covers the essential calling context: what results contain (catalog entries), the fni_score field semantic, the bounded result set, the limit/query/task semantic, retry behavior, and mutation safety. An agent has everything it needs to decide whether to invoke this tool and how to handle the response.
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 starts at 3 since the schema already documents all three parameters. The description adds value beyond the schema by explaining the mechanics of how task and query interact ('task text folded into the query') and how the parameters relate to the returned scoring field ('The score used for ordering may differ from the fni_score field'). It compensates fine for the low parameter-name ambiguity.
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 opening sentence names a specific verb and resource: 'Keyword-search AI entities using the task/query text as input and return matching catalog entries.' It then actively distinguishes itself from the sibling free2aitools_search ('Mechanically this is the same keyword search... with the task text folded into the query') and states what it is NOT (not task-fit recommendation, not an inference router). An agent can tell exactly what this tool does without opening the schema.
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 provides an explicit USE WHEN condition ('USE WHEN you have task text and want catalog entries ordered by that relevance score'), names the alternatives that should be selected instead ('Use free2aitools_search for plain keyword discovery, or free2aitools_select_model to apply hardware/license metadata filters'), and lists exclusions. This is complete routing guidance with nothing left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword discovery over the Free2AITools catalog of AI models, datasets, papers, and tools. Returns matching catalog entries (metadata). Search results are ordered by a relevance score based on the FNI (Free2AITools Nexus Index) and, where term-match data is available, how well the entry matches the query. The score used for ordering may differ from the fni_score field returned in the response. The result set is bounded. The FNI is a 5-factor score: Semantic relevance, Authority, Popularity, Recency, Quality. The Semantic factor is a query-time baseline, not a live per-entity measurement (fni_s is returned null with a note). USE WHEN you need to discover which AI entities exist for a topic or keyword. DO NOT USE for general web search, to run/call/execute a model, to get a generated or inferred answer, or to route to an inference provider — this returns catalog metadata only, for the calling agent to reason over and decide on. Free discovery catalog: results are never paid placement / sponsored, and there is no billing or payment. Read-only, no side effects. May return a retryable transient 503 under cold-path or fallback budget limits; retry according to Retry-After. Use free2aitools_select_model instead when you have specific hardware or license constraints.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Filter by entity type (default: all) | |
| limit | No | Max results to return (1-20, default 10) | |
| query | Yes | Natural language search query (e.g. "code generation", "image segmentation") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden — and it delivers: ordering semantics (FNI plus term-match), the note that the ordering score may differ from the returned fni_score field, result-set bounding, the 5-factor FNI composition with the caveat that the semantic factor is a query-time baseline with fni_s returned null, read-only/no-side-effects guarantees, absence of paid placement, and retryable 503 behavior with Retry-After guidance.
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 long but every clause earns its place: purpose, ordering behavior, caveats, usage routing, and error semantics are each distinct and non-redundant. It is front-loaded with the core discovery action and scoping before delving into scoring mechanics and exclusions.
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?
With no annotations and no output schema, this description covers everything an agent needs to invoke the tool safely, interpret a non-obvious ordering behavior, and understand the empty/null-return scenarios. The only residual gaps, such as exact return shape details, are minor given 'metadata' is stated and the 3-parameter schema fully documents inputs.
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 even with no additional parameter info in the description. The description's relevance-ordering semantics enriches understanding of query behavior slightly, and the catalog scope implies some of the type enum values, but it does not add parameter-specific syntax or format guidance beyond what the schema already documents.
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 opens with a specific verb and resource: 'Keyword discovery over the Free2AITools catalog of AI models, datasets, papers, and tools. Returns matching catalog entries (metadata).' This precisely distinguishes it from siblings like free2aitools_compare, free2aitools_explain, and free2aitools_rank, and from free2aitools_select_model, which is explicitly named as the alternative for entity selection.
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?
Provides explicit 'USE WHEN' and 'DO NOT USE' conditions, covering general web search, executing models, generating answers, and routing to inference providers. It also names the exact alternative tool and the condition that selects it: 'Use free2aitools_select_model instead when you have specific hardware or license constraints.' Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filter the Free2AITools catalog by declared hardware/license metadata and return FNI-ranked candidate entries. USE WHEN you have concrete constraints (VRAM, params, license, context length, local-runnability) and want candidates narrowed by them. Constraints are metadata/heuristic filters over stored fields, NOT verified compatibility analysis, model inference, or model execution; this tool does not decide for you and is not an inference router. The caller is responsible for the final selection. Results are FNI-ranked, never paid placement, with no billing. Read-only, no side effects. Use free2aitools_search for unconstrained keyword discovery, or free2aitools_rank for keyword ranking without metadata filters.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Task name or natural language description (e.g. "text-generation", "code assistant", "image classification") | |
| limit | No | Max entries returned (1-20, default 5) | |
| explain | No | Include per-entry fni_summary (factual FNI factor/spec facts) and caveats in the response (default true) | |
| constraints | No | Hardware and license filters (all optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses read-only, no side effects, FNI-ranked non-paid, no billing. Acknowledges heuristic/metadata filtering limitations ('does not verify actual compatibility'). No annotations provided, so description carries full burden. Minor omission: no mention of pagination or error behavior, but constraints 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is front-loaded with purpose and usage, followed by limitations and alternatives. Only three sentences with no filler. Could slightly reduce redundancy (e.g., repeated 'heuristic'), but overall efficient.
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 full schema coverage (4 params, 1 required) and no output schema, description covers tool behavior, result ranking, and parameter explanation. Could benefit from brief mention of return structure (e.g., array of model entries), but the 'explain' parameter hints at per-entry summaries. Adequate for complexity.
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%, so baseline is 3. Description adds value by explaining that constraints are metadata/heuristic filters, not verified runtime checks. Also notes that results are FNI-ranked and includes explanation parameter. Slightly above baseline due to clarifying context 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?
Description clearly states the tool filters catalog by metadata/constraints and returns FNI-ranked candidates. It distinguishes from siblings: unconstrained search (free2aitools_search) and keyword ranking (free2aitools_rank). Specific verb 'filter' and resource 'Free2AITools catalog.'
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 'USE WHEN you have concrete constraints' and provides clear exclusions: not for inference, not a decision tool, not verified compatibility. Names alternative tools for different use cases. Complete guidance.
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.
free2aitools_compare1 field changedInput schema / properties / ids / descriptionPrevious value: -"Entity IDs to compare (2-25). Use model_id from select_model results or id from search results (e.g. [\"hf-model--meta-llama--llama-3-8b\", \"hf-model--google--gemma-2-27b\"])"New value: +"Catalog entity IDs to compare (2-25), any entity type. Use the id from search/rank/select_model results verbatim (e.g. [\"hf-model--meta-llama--llama-3-8b\", \"arxiv--2401.00001\"])"free2aitools_select_model4 fields changedInput schema / properties / constraints / properties / can_run_local / descriptionPrevious value: -"Only models that can run locally"New value: +"Heuristic local-runnability filter based on stored metadata such as model size and GGUF indicators. Does not verify actual runtime compatibility on the caller hardware or framework."Input schema / properties / constraints / properties / ollama_compatible / descriptionPrevious value: -"Only models runnable via Ollama"New value: +"Heuristic filter on stored metadata (GGUF indicators). Does not verify actual Ollama runtime compatibility on the caller hardware."Input schema / properties / explain / descriptionPrevious value: -"Include per-model rationale text (default true)"New value: +"Include per-entry fni_summary (factual FNI factor/spec facts) and caveats in the response (default true)"Input schema / properties / limit / descriptionPrevious value: -"Max recommendations (1-20, default 5)"New value: +"Max entries returned (1-20, default 5)"free2aitools_search1 field changedInput schema / properties / type / enumPrevious value: -[
- "all",
- "model",
- "tool",
- "dataset",
- "paper"
-]New value: +[
+ "all",
+ "model",
+ "tool",
+ "dataset",
+ "paper",
+ "benchmark"
+]free2aitools_compare1 field changedInput schema / properties / ids / descriptionPrevious value: -"Entity IDs to compare (2-10). Use model_id from select_model results or id from search results (e.g. [\"hf-model--meta-llama--llama-3-8b\", \"hf-model--google--gemma-2-27b\"])"New value: +"Entity IDs to compare (2-25). Use model_id from select_model results or id from search results (e.g. [\"hf-model--meta-llama--llama-3-8b\", \"hf-model--google--gemma-2-27b\"])"free2aitools_rank1 field changedInput schema / properties / constraintsRemoved value: -{
- "description": "Optional keyword filters applied to results",
- "items": {
- "type": "string"
- },
- "type": "array"
-}free2aitools_compare1 field changedInput schema / properties / ids / descriptionPrevious value: -"Model IDs to compare (2-10)"New value: +"Entity IDs to compare (2-10). Use model_id from select_model results or id from search results (e.g. [\"hf-model--meta-llama--llama-3-8b\", \"hf-model--google--gemma-2-27b\"])"free2aitools_explain1 field changedInput schema / properties / id / descriptionPrevious value: -"Entity name or ID to explain (e.g. \"Llama-3\")"New value: +"Entity name or ID to explain (e.g. \"Llama-3\", \"hf-model--meta-llama--llama-3-8b\")"free2aitools_rank4 fields changedInput schema / properties / constraints / descriptionAdded value: +"Optional keyword filters applied to results"Input schema / properties / limit / descriptionAdded value: +"Max results to return (1-20, default 10)"Input schema / properties / query / descriptionAdded value: +"Search query describing what to rank (e.g. \"text generation\", \"object detection\")"Input schema / properties / task / descriptionPrevious value: -"The task to rank tools for"New value: +"Optional task context to combine with query for more targeted ranking"free2aitools_search3 fields changedInput schema / properties / limit / descriptionAdded value: +"Max results to return (1-20, default 10)"Input schema / properties / query / descriptionPrevious value: -"Natural language search query"New value: +"Natural language search query (e.g. \"code generation\", \"image segmentation\")"Input schema / properties / type / descriptionAdded value: +"Filter by entity type (default: all)"free2aitools_select_model12 fields changedInput schema / properties / constraints / descriptionAdded value: +"Hardware and license filters (all optional)"Input schema / properties / constraints / properties / can_run_local / descriptionAdded value: +"Only models that can run locally"Input schema / properties / constraints / properties / hosted_on / descriptionAdded value: +"Hosting platform filter (e.g. \"hf-inference\")"Input schema / properties / constraints / properties / license / descriptionAdded value: +"Specific license (e.g. \"Apache-2.0\", \"MIT\")"Input schema / properties / constraints / properties / license_type / descriptionAdded value: +"License category filter"Input schema / properties / constraints / properties / max_params_b / descriptionAdded value: +"Maximum model parameters in billions"Input schema / properties / constraints / properties / max_vram_gb / descriptionAdded value: +"Maximum GPU VRAM in GB (e.g. 8, 24)"Input schema / properties / constraints / properties / min_context_length / descriptionAdded value: +"Minimum context window in tokens"Input schema / properties / constraints / properties / ollama_compatible / descriptionAdded value: +"Only models runnable via Ollama"Input schema / properties / explain / descriptionAdded value: +"Include per-model rationale text (default true)"Input schema / properties / limit / descriptionAdded value: +"Max recommendations (1-20, default 5)"Input schema / properties / task / descriptionPrevious value: -"Task name or description (e.g. \"text-generation\", \"code assistant\")"New value: +"Task name or natural language description (e.g. \"text-generation\", \"code assistant\", \"image classification\")"free2aitools_comparefree2aitools_explainfree2aitools_rankfree2aitools_searchfree2aitools_select_modelClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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