Statement to Budget

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this thoroughly by explaining access levels, the preview limitation, unlock_url pricing, row masking, processing time, and the polling behavior. It also reveals the 'full' vs 'preview' distinction based on API key credit, which is essential for setting expectations.

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 but well-structured: the core purpose is front-loaded, and subsequent sentences cover return behavior, limitations, pricing, and polling. It is longer than average, but every clause carries meaningful operational information, so the length is justified.

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 does an excellent job of explaining the return values (session_id, access.level, unlock_url, status) and the asynchronous behavior. It also covers edge cases like large statements and password-protected PDFs. An agent has enough contextual information to invoke the tool and handle the response 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by enumerating the accepted file formats (PDF, CSV, XLS, XLSX, OFX), clarifying that content is base64, and tying the password parameter to locked PDFs. This is useful supplement rather than duplication.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Upload a bank statement ... and run AI extraction + categorisation'), names the resource and supported formats, and clearly differentiates this tool from siblings by making its role as the upload/extraction entry point obvious. It is immediately clear what the tool does and how it relates to get_job.

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 gives clear guidance on when to use the tool: to upload a statement and start extraction. It also provides a conditional alternative: if status is 'processing', poll get_job. It does not explicitly mention get_transactions, but the primary usage context and the relationship to get_job are well covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral burden. It does disclose that the status can be processing, complete, or error, which is useful. However, it does not describe the response shape, error details, or whether repeated polling is expected beyond the implication of the word 'Poll'.

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 short sentences with no filler, front-loading the action and the key statuses. Every word contributes to understanding the tool's core behavior.

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 one-parameter polling tool, the description is close to sufficient, and listing the statuses hints at the return value. However, without an output schema or annotations, it should also mention where session_id comes from and what a complete or error response contains.

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 0%, so the description must compensate. It does indicate that session_id identifies the conversion job, adding a bit of meaning beyond the bare string type. It does not explain how to obtain session_id, such as from a convert_statement response.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Poll') and a specific resource ('a conversion job'), and it lists the possible statuses, so the tool's purpose is clear. It does not explicitly differentiate from sibling tools like convert_statement or get_transactions, but the function is distinct enough by name and phrasing.

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 gives no guidance on when to use this tool versus convert_statement or get_transactions, and it does not mention prerequisites such as where session_id comes from. The word 'Poll' only implies that this is for checking job status after submission, but the tool does not say that explicitly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations available, the description carries the full transparency burden and does a solid job: it discloses conditional behavior for preview sessions versus completed sessions, mentions the 5-sample-row limit, locked_rows, and unlock_url. It does not cover error cases or authentication, but for a simple fetch tool this is meaningful behavioral detail.

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 compact sentences: the first front-loads the core function, and the second adds the key behavioral nuance. Every phrase adds information, and there is no redundant or filler wording.

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 one-parameter tool with no output schema, the description covers the main call contexts, including completed sessions and preview sessions, and lists the returned components. It could be more complete by specifying expected error behavior for invalid session IDs, but the essential usage context is present.

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 zero description coverage, and the tool description does not explain the format, origin, or constraints of session_id beyond implying it identifies a session. The session concept is indirectly clarified by the completed/preview distinction, but that is too weak to compensate for a completely undocumented required parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Fetch') and resource ('categorised transactions and a spending summary for a completed session'), which makes the tool's purpose immediately clear. It also distinguishes from siblings like get_job by focusing on transaction data and session completion state, including concrete preview behavior.

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 gives clear context that the tool is intended for completed sessions and separately describes what happens for preview sessions. It does not explicitly compare against convert_statement or get_job, but the session-specific wording is enough to guide typical use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.