Inspect Fireworks AI models, deployments, datasets and fine-tuning jobs.
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Average 3.7/5 across 14 of 14 tools scored.
Each tool has a distinct resource and action combination (e.g., create_dataset vs delete_deployment vs list_models), with no overlapping purposes. Descriptions clearly differentiate each tool.
All tools follow a consistent 'fireworks_verb_noun' pattern, with verbs like create, delete, get, list and singular or plural nouns as appropriate. No mixing of conventions.
14 tools cover the main resources of the Fireworks platform (accounts, datasets, deployments, fine-tuning jobs, models, batch inference, users). The scope is appropriate and not overwhelming.
The tool set is heavily read-oriented with only one creation tool (create_dataset) and one deletion tool (delete_deployment). Missing create for deployments, fine-tuning jobs, models; missing update and delete for most resources. Gaps would hinder full workflow automation.
fireworks_create_datasetCreate datasetBCreates a dataset metadata record in Fireworks (additive; actual data is uploaded separately). Control-plane: POST /v1/accounts/{account_id}/datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| filter | No | Filter applied when deriving from a source dataset. | |
| format | No | Dataset format: CHAT | COMPLETION | RL. | |
| datasetId | Yes | Id for the new dataset (the resource segment). | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. | |
| displayName | No | Human-readable display name. | |
| externalUrl | No | External source URL for the dataset data. | |
| exampleCount | No | Number of examples in the dataset. | |
| sourceDatasetId | No | Id of a source dataset to derive from. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims the operation is 'additive,' suggesting it is not destructive, but the annotations set 'destructiveHint: true,' creating a direct contradiction. The description fails to disclose this behavioral trait accurately, misleading the agent.
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 consists of two concise sentences: one explaining the core function and additive nature, the other providing the endpoint. No redundant information, and key points are front-loaded.
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 description complements the schema with the endpoint and additive note, the contradiction with annotations undermines completeness. Additionally, lacking usage guidance and behavioral clarity, the description does not fully equip the agent for correct invocation despite schema richness.
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 description does not need to add parameter semantics. The description provides no additional parameter-specific meaning beyond the endpoint path (which is already in annotations implicitly). 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 it creates a dataset metadata record, specifies that actual data is uploaded separately (additive), and provides the REST endpoint. This distinguishes it from sibling tools like list/get/delete, making the purpose unambiguous.
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 includes the clue 'additive; actual data is uploaded separately,' implying this tool is for metadata creation before data upload. However, it does not explicitly state when to use this tool versus alternatives (e.g., no mention of not using it for data upload or comparison with other dataset operations).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_delete_deploymentDelete deploymentADELETES (shuts down) a Fireworks deployment — stops serving that model and stops its billing. Destructive. Control-plane: DELETE /v1/accounts/{account_id}/deployments/{deployment_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. | |
| deployment_id | Yes | Deployment id to delete (the resource segment). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotation destructieveHint=true, the description adds specific behavioral details: it 'shuts down' the deployment, stops billing, and includes the HTTP method and path. This provides context that the annotation alone does not offer, earning a score above baseline.
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 extremely concise: one sentence conveying the action, effect, destructiveness, and API endpoint. Every word adds value with no redundancy. The key information is front-loaded with the capital 'DELETES', ensuring immediate clarity.
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 destructive nature, the description adequately covers the action, consequences, and the HTTP mapping. There is no output schema, but that is typical for delete operations. The description is sufficient for an agent to understand the tool's complete effect without needing additional 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?
The input schema has 100% description coverage for both parameters. The description does not add new meaning beyond the schema; it mentions the HTTP path but does not clarify the parameters' roles or formats. Thus, the description adds no additional semantic value, resulting in the baseline score.
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 verb 'DELETES', the resource 'Fireworks deployment', and the effects 'stops serving that model and stops its billing'. It distinguishes itself from sibling tools that perform get, list, or create actions, leaving no ambiguity about the tool's function.
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 labels the tool as 'Destructive' and explains the consequences of deletion, but it does not provide explicit guidance on when to use it versus alternatives or state any prerequisites. While the destructive nature is clear, the description lacks explicit when-not-to-use criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_get_accountGet accountAGet the Fireworks account record (display name, state, etc.). Control-plane: GET /v1/accounts/{account_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, indicating a safe read operation. Description adds the API endpoint and return field examples, but does not go beyond that. No contradictions.
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 core purpose, no extraneous words. Efficient and clear.
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 get tool with one optional parameter and no output schema, the description adequately states what it returns and the API endpoint. Minor improvement could include noting the full account object 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 has 100% coverage; the description mentions account_id implicitly via the endpoint path but adds no extra semantic context beyond what the schema provides.
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 tool retrieves the Fireworks account record with fields like display name and state, and distinguishes it from sibling tools which operate on datasets, deployments, etc.
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?
Implies use for getting account info but lacks explicit guidance on when to use vs. alternatives or when not to use. Sibling tools are sufficiently different, so no confusion, but no 'when to use' language.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_get_datasetGet datasetAGet a single dataset by id. Control-plane: GET /v1/accounts/{account_id}/datasets/{dataset_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. | |
| dataset_id | Yes | Dataset id (the resource segment). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true, so the description does not need to state safety. It adds the HTTP endpoint and path, which is consistent and reveals a bit about the API interaction, but no deeper behavioral traits (e.g., no pagination, no 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no unnecessary words. It is front-loaded with the core purpose, then adds the HTTP reference. Every sentence earns its place.
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 is low (simple retrieval, no nested objects, no output schema), and annotations plus schema cover safety and parameters, the description is adequate. It lacks details on return format or error handling, but that's acceptable without 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?
Schema coverage is 100% and both parameters have descriptions explaining their purpose (account_id override and dataset_id resource segment). The description adds no extra meaning beyond the schema, so 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 explicitly states 'Get a single dataset by id.' This clearly indicates the action (get) and the resource (single dataset) with specific identification via id. It distinguishes from sibling tools like fireworks_list_datasets.
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 does not provide explicit guidance on when to use this tool versus alternatives like fireworks_list_datasets. The use case is implied (retrieving one dataset by id) but not clearly contrasted with listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_get_deploymentGet deploymentAGet a single deployment by id. Control-plane: GET /v1/accounts/{account_id}/deployments/{deployment_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. | |
| deployment_id | Yes | Deployment id (the resource segment). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, and the description does not contradict this. The added detail about the Control-plane endpoint provides some behavioral context. However, it omits potential side effects, error conditions, or rate limits, which are less critical for a read operation but still relevant for full transparency.
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 core purpose, and no extraneous words. It efficiently conveys the operation and the REST endpoint.
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 operation with two parameters and no output schema, the description covers the essential behavior. It could mention that the response is a deployment object, but that is often inferred. Given the tool's simplicity and the annotation coverage, it is largely sufficient.
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 schema already documents both parameters. The description adds the endpoint path but does not provide additional semantic meaning for the parameters beyond what is in 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 'Get a single deployment by id,' specifying the exact verb ('Get'), resource ('deployment'), and uniqueness ('single ... by id'). This distinguishes it from sibling tools like fireworks_list_deployments (list) and fireworks_delete_deployment (delete).
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 retrieving a specific deployment by ID but lacks explicit guidance on when to use this vs. alternatives (e.g., list_deployments for multiple, get_account for accounts). The behavior is straightforward, so no strong exclusions are needed, but the absence of contextual hints is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_get_fine_tuning_jobGet fine-tuning jobAGet a single supervised fine-tuning job by id. Control-plane: GET /v1/accounts/{account_id}/supervisedFineTuningJobs/{job_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Supervised fine-tuning job id (the resource segment). | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint=true, so the read-only nature is known. Description adds the HTTP path info, which provides some context but no additional behavioral traits like error handling or throttling.
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 concise sentences with no redundant information. Every word adds value.
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 get-by-id tool, the description is nearly complete. It mentions the API endpoint pattern. However, it does not describe return values or error cases, which would be helpful but not critical given the tool's simplicity.
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 clear parameter descriptions. The description does not add meaning beyond what's in the schema, so 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?
Description clearly states 'Get a single supervised fine-tuning job by id', specifying the verb (get) and resource (fine-tuning job). This distinguishes it from sibling tools like fireworks_list_fine_tuning_jobs.
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?
Description provides no guidance on when to use this tool versus alternatives, nor any prerequisites or conditions. It only states the basic operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_get_modelGet modelAGet a single model by id. Control-plane: GET /v1/accounts/{account_id}/models/{model_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | Model id (the resource segment, e.g. 'llama-v3p1-8b-instruct'). | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, so the description does not need to reiterate read-only behavior. It adds minimal behavioral context beyond the endpoint reference (GET). For a simple retrieval, this is adequate but not enriched.
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 concise with two sentences: the first states the core functionality, the second provides the REST endpoint. No unnecessary 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 simplicity of a get operation with 2 parameters and annotations, the description is sufficient. It lacks return format details but that is acceptable as output schema is absent and the tool is straightforward.
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 both parameters described adequately in the schema. The description does not add further parameter details, so it meets the baseline of not needing to compensate.
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 explicitly states 'Get a single model by id,' clearly indicating the verb (get) and resource (model). This distinguishes it from siblings like fireworks_list_models, which lists multiple models, and other get tools for different resources (account, dataset, deployment).
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 does not explicitly state when to use this tool vs alternatives like fireworks_list_models. It implies use for retrieving a specific model by ID, but lacks direct comparisons or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_list_batch_inference_jobsList batch inference jobsAList batch inference jobs in the account. Control-plane: GET /v1/accounts/{account_id}/batchInferenceJobs.
| Name | Required | Description | Default |
|---|---|---|---|
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true. The description adds the API endpoint but no behavioral details (e.g., pagination, rate limits). With annotations, this is adequate.
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 concise sentences, no fluff. Front-loaded with the core purpose.
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 list tool with good schema and annotations, the description is minimally adequate but does not describe return format or pagination behavior.
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 3. The description does not add any parameter meaning beyond the schema's existing 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?
The description clearly states the verb (List) and resource (batch inference jobs), and the resource name distinguishes it from sibling list tools for datasets, deployments, etc.
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 on when to use this tool versus alternatives; no exclusions or context beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_list_datasetsList datasetsAList datasets in the account. Control-plane: GET /v1/accounts/{account_id}/datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is established. The description adds that this is a control-plane GET request, which is consistent. However, it does not disclose pagination behavior or rate limits beyond the schema's pageSize maximum. Given the annotations, the description adds minimal additional 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 extremely concise: two sentences with no fluff. It front-loads the purpose and includes the API endpoint for reference. Every word earns its place.
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 list tool with 3 parameters and no output schema, the description is incomplete. It doesn't mention the response structure (e.g., returns a list of dataset objects with nextPageToken). While the annotations and schema cover safety and parameters, the lack of output description leaves the agent without full 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?
Input schema has 100% description coverage, so parameters are fully documented. The description does not add any extra meaning beyond what the schema provides. Baseline 3 is appropriate since the schema already carries the burden.
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 'List datasets in the account' with a specific verb and resource. It also provides the API endpoint, leaving no ambiguity. This distinguishes it from sibling tools like fireworks_get_dataset (single dataset) and fireworks_create_dataset (create).
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. For example, it doesn't mention when to use list vs get_dataset or how pagination might affect usage. The description is minimal and lacks decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_list_deployed_modelsList deployed modelsAList deployed models (model↔deployment bindings). Control-plane: GET /v1/accounts/{account_id}/deployedModels.
| Name | Required | Description | Default |
|---|---|---|---|
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description supplements the 'readOnlyHint' annotation by specifying the HTTP method 'GET' and labeling it a 'Control-plane' operation. This adds context about the nature of the call, which is beyond what the annotation alone provides. No destructive behavior is mentioned, consistent with the annotation.
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 with no fluff. The first sentence states the purpose, and the second provides the API endpoint. Every sentence earns its place.
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 tool has no output schema, so the description should ideally hint at the return format. It does not. The pagination parameters imply pagination, but the response structure (e.g., nextPageToken) is not mentioned. Overall adequate but lacks completeness for a tool without 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 input schema covers all three parameters with descriptions (100% coverage), so the baseline is 3. The description mentions the endpoint '{account_id}' but adds no new parameter-specific meaning beyond the schema. The value added is marginal.
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 'List deployed models' and elaborates with '(model↔deployment bindings)', providing a specific verb and resource. It distinguishes from sibling tools like 'fireworks_list_models' and 'fireworks_list_deployments' by clarifying the scope as bindings between models and deployments.
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 does not provide guidance on when to use this tool versus alternatives. It neither explains the ideal use case nor mentions when not to use it. Sibling tools such as 'fireworks_list_models' and 'fireworks_list_deployments' exist, and the description offers no criteria for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_list_deploymentsList deploymentsAList deployments (serving instances) in the account. Control-plane: GET /v1/accounts/{account_id}/deployments.
| Name | Required | Description | Default |
|---|---|---|---|
| filter | No | Server-side filter expression. | |
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the read-only nature is clear. The description adds the control-plane endpoint detail but does not disclose other behavioral traits such as pagination behavior or rate limits. With annotations present, the description provides minimal additional 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?
The description is very concise with one sentence and an endpoint specification. It is front-loaded with the core action. However, the title is redundant with the description.
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 simplicity of the tool (4 optional parameters, no output schema), the description is adequate for basic use. However, because there is no output schema, the description should ideally mention the return structure or pagination behavior, which it does not.
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 does not add any extra meaning beyond what the input schema already provides for the parameters. The schema descriptions are detailed (e.g., 'Server-side filter expression', 'Opaque page token').
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 deployments') and the resource ('deployments (serving instances)'). It distinguishes from sibling tools like `fireworks_get_deployment` and `fireworks_delete_deployment` by specifying the scope 'in the account' and the control-plane endpoint.
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 listing all deployments but does not explicitly state when to use this tool versus alternatives like `fireworks_get_deployment` (for a single deployment) or `fireworks_delete_deployment`. No exclusions or usage context are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_list_fine_tuning_jobsList fine-tuning jobsAList supervised fine-tuning jobs in the account. Control-plane: GET /v1/accounts/{account_id}/supervisedFineTuningJobs.
| Name | Required | Description | Default |
|---|---|---|---|
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the API endpoint and scope ('in the account'), which is minimal but not contradictory. Behavioral traits beyond annotations are not disclosed.
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 extremely concise with two short sentences that front-load the core purpose. Every word is necessary, and there is no extraneous information.
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 adequately states the purpose but does not mention the return format (list of jobs) or pagination behavior beyond the pageToken parameter. Given the simplicity of the tool, this is minimally sufficient but could be improved.
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 each parameter is already well-documented in the schema. The tool description does not add any additional meaning or context for parameters beyond what is provided in the 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?
The description clearly states the action ('List') and the specific resource ('supervised fine-tuning jobs in the account'), distinguishing it from siblings like fireworks_list_batch_inference_jobs. The inclusion of 'supervised' adds specificity.
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 (e.g., fireworks_get_fine_tuning_job for a single job). The description lacks context for common 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.
fireworks_list_modelsList modelsAList models in the account (base + fine-tuned). Control-plane: GET /v1/accounts/{account_id}/models.
| Name | Required | Description | Default |
|---|---|---|---|
| filter | No | Server-side filter expression. | |
| orderBy | No | Field to order by, e.g. 'createTime desc'. | |
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. The description adds the HTTP method (GET), which is behavioral context but not critical. No details on pagination behavior or listing semantics are provided, but the read-only nature is clear.
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 concise sentences: one for purpose and one for implementation detail (HTTP endpoint). No unnecessary words or repetition.
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 states it lists both base and fine-tuned models, but does not explain pagination behavior despite having pageSize and pageToken parameters. No output schema exists, so return structure is omitted. Adequate but not thorough.
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 schema already documents all parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or usage constraints.
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 verb 'List' and the resource 'models in the account', and specifies the scope (account) and types (base + fine-tuned). It effectively distinguishes this tool from siblings like fireworks_list_datasets and fireworks_list_deployments.
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 on when to use this tool versus alternatives such as fireworks_get_model or fireworks_list_deployed_models. The description does not provide context for selecting this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fireworks_list_usersList usersAList account members (users). Control-plane: GET /v1/accounts/{account_id}/users.
| Name | Required | Description | Default |
|---|---|---|---|
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation indicates readOnlyHint: true, and the description adds the HTTP method and path, confirming a read-only operation. It does not contradict annotations and provides additional context beyond them.
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 extremely concise with two sentences, the first stating the purpose and the second providing the API endpoint. No extraneous information is present.
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 does not mention the return format (e.g., list of user objects, pagination behavior) despite the schema including pagination parameters. For a list tool, this omission reduces completeness.
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 parameters are well-documented in the schema. The description implicitly links account_id to the URL path, adding some semantic context beyond the schema, justifying above 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 lists account members (users) and provides the specific HTTP method and path, effectively distinguishing it from sibling list tools like fireworks_list_datasets and fireworks_list_models.
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, such as when to list users instead of deployments or datasets. The description does not include any when-to-use or when-not-to-use indicators.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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