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

With no annotations, the description carries the full behavioral burden, and it discloses very little: it implies a file write but says nothing about permissions, whether appends are atomic, what happens to the file on validation failure, or whether the write is reversible. "Validated" is the only behavioral hint and it is not explained.

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?

A single front-loaded sentence with no filler or redundancy. It is efficient, though arguably under-specified rather than truly concise given the 4 undocumented required parameters.

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?

An output schema exists, so return values need no explanation, but for a required-parameter mutation tool with no annotations and no parameter documentation, the description leaves too much unstated: validation rules, overwrite/duplicate behavior, and permission requirements.

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% across 4 parameters, and the description names none of them (name, role, department, salary) nor adds format or constraint meaning beyond what the field titles already imply. It does not compensate for the coverage gap.

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?

Specific verb ("append") plus resource ("a validated employee record") and the concrete storage target (employees.csv). It clearly states what the tool does, though it does not differentiate itself from the company-data siblings, which are mostly unrelated to employee records.

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?

There is no statement of when to use this tool versus alternatives, no prerequisites, and no indication of what happens on duplicate or invalid records. The word "validated" hints that validation occurs, but the conditions and failure behavior are left entirely to inference.

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?

No annotations are provided, so the description carries the full burden. It does disclose two meaningful behavioral facts — that the operation is additive ('append', not overwrite) and that state is persisted to financials.json — but says nothing about permissions, validation failures, or duplicate handling.

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?

A single tightly written sentence with the verb and target front-loaded and no filler. It is efficient, though almost too terse to carry the needed guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need not be explained, but for an unannotated mutation tool with four undocumented required parameters the description is too thin. Persistence target is given but usage context, prerequisites, and parameter meaning are all missing.

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% across four required parameters. The description only echoes the type enum ('income or expense') and adds no meaning for category format, amount constraints, or description content, so it fails to compensate for the gap.

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?

States a specific verb (append) plus resource (income or expense transaction) and the target artifact (financials.json). An agent can tell it is a financial-entry writer, though it does not explicitly differentiate itself from the sibling mutation tools like update_company_notes or migrate_company_data.

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 indication of when to use this tool versus alternatives, no prerequisites, and no exclusions. With ten sibling tools in the same company-data family, the absence of routing guidance leaves the agent to infer context entirely.

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 carries the full burden and does well: it discloses that the call runs as a dry run unless apply=True and that each changed file is backed up first. It omits permission requirements, reversibility, and how unmapped/untouched fields are treated, so it falls short of full behavioral coverage.

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?

Three tight sentences, front-loaded with the write action followed by input contract and then safety behavior. No filler and no repetition of schema fields.

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?

An output schema exists, so return values need not be explained, and the description covers the write action, input contract, dry-run default, and backup behavior. Remaining gaps are peripheral: which files/workspace are targeted and prerequisites beyond having run the plan tool.

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, and it does: it defines the keying convention for 'answers' (keyed by the plan item's key, e.g. 'company_profile.json:mission'), the sentinel semantics of true, and the effect of 'apply' (dry run vs. write). It does not describe the accepted value types beyond the true-sentinel rule.

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?

States a specific verb+resource ('Write a confirmed company_profile.json and financials.json mapping') and positions itself relative to the sibling that produces its inputs (plan_company_data_migration). An agent can distinguish this from plan_company_data_migration and migrate_company_data without opening any 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?

Gives clear workflow context: answers must correspond to open items reported by plan_company_data_migration, keyed by that item's key. It does not explicitly say 'call plan first' or exclude alternatives like migrate_company_data, so routing is inferred rather than stated.

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 carries the full behavioral burden, and it does disclose a non-obvious side effect: a single call creates three entities (profile, ledger, founder employee). That is genuinely useful. However, it says nothing about failure modes (e.g., duplicate company name), permission requirements, or reversibility, which are significant gaps for an unannotated mutation 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?

A single front-loaded sentence with zero filler; the verb and the three created artifacts are stated up front. Nothing is padded and nothing needs trimming.

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?

An output schema exists, so return values need not be explained, which the description correctly omits. But for an unannotated, three-parameter mutation that provisions multiple entities, the definition omits prerequisite conditions and parameter meaning, leaving real gaps for the agent.

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?

All three parameters have 0% schema description coverage, so the description must compensate, and it does not. It never clarifies what initial_budget represents (currency, units, minimum 0 semantics) or what sector expects beyond a free string. Parameter meaning is left entirely to inference.

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 gives a specific verb (Create) and enumerates exactly what gets created: a company profile, a financial ledger, and a founder employee record. This implicitly distinguishes it from siblings like add_financial_record and add_employee, which add single entities rather than bootstrapping a whole company. It stops short of naming a sibling explicitly, but the scope is 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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives such as add_employee or add_financial_record, which overlap with the founder record and ledger this tool creates. An agent can infer 'use this to start a company,' but the description never states when this is preferable to the granular sibling tools.

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 carries the full behavioral burden. 'Return' implies a read-only operation and 'from local files' usefully discloses the data source, but it says nothing about freshness guarantees beyond the word 'current', permissions, or behavior when no local data exists.

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?

A single front-loaded sentence with no filler; the action, the payload, and the source are all stated without waste.

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 is parameterless and has an output schema, so return values need not be described. The remaining gap is sibling differentiation against inspect_company_data and read_company_file, which is the main thing an agent would want clarified.

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 tool takes zero parameters, so there is nothing for the description to compensate for. The baseline of 4 applies since parameter semantics are trivially complete.

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?

States a specific verb ('Return') and concrete resources ('company profile and financial summary'), plus a data-source scope ('from local files'). However, it does nothing to distinguish itself from siblings like inspect_company_data or read_company_file, which an agent must disambiguate among.

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?

There is no when-to-use or when-not-to-use guidance and no named alternative. The agent must infer from the phrase 'company profile and financial summary' that this is the summary-level tool rather than the file-level siblings, which is a real inference burden given the crowded sibling set.

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?

No annotations are provided, so the description carries the full burden. It does not state whether this is read-only, whether it reports or repairs mismatches, or whether it has side effects on company_data — all material for a validation tool whose siblings include mutating operations.

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?

A single tight sentence that front-loads the action and the scope. Nothing extraneous.

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?

An output schema exists, so return values need not be described. For a zero-argument read tool, the description answers what it does but leaves the read-only nature and the circumstances that warrant running it unstated, which matters given the migration siblings.

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 tool takes zero parameters, so there is nothing for the description to disambiguate beyond what the schema already establishes. Baseline 4 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?

States a concrete verb (check) and the resource (the files in company_data) plus the specific criterion (match the schema the tools expect). This is clear, though it does not explicitly differentiate itself from related siblings like migrate_company_data or plan_company_data_migration.

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 run this versus the migration-planning siblings, and no explicit prerequisites (e.g., run before planning a migration, or after creating a company). The diagnostic intent is only implied by the word 'check'.

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?

No annotations are provided, so the description carries the burden. It conveys that this is a non-destructive listing and names the returned attributes, but says nothing about pagination, directory recursion, permissions, or behavior on large datasets.

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?

A single, front-loaded sentence with no filler; the key scope ('all files in company_data') comes first and each clause adds 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?

For a simple zero-parameter listing tool with an output schema that already defines the return shape, the description is nearly sufficient. It could still note scope limits (e.g., recursion or result caps), which keeps it short of 5.

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 tool takes zero parameters, so the schema baseline is 4. The description adds nothing about parameters, but none exist to document.

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?

States a specific verb (List) and resource (files in company_data), plus the returned attributes. It is clearly distinguishable from read_company_file (retrieve content) and inspect_company_data (analysis), though it does not name a sibling explicitly.

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 scope 'all files in company_data' implies usage for file inventory, but there is no explicit when-to-use or when-not-to-use guidance, and no routing to alternatives like read_company_file or inspect_company_data.

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 present, the description carries the full burden and does so well: it discloses the safe-by-default dry run, the exact mutation performed under apply=True (renames known columns, fills start_date/status defaults), that unknown columns like performance_score are preserved, and that a timestamped backup is written first. This is precisely the non-destructive/reversibility context an agent needs before a data migration.

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?

Four short sentences, front-loaded with the purpose, then the safety default, then the concrete effects of apply=True. Every sentence adds information an agent needs; no filler.

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?

An output schema exists, so return values need not be described, and the input side is fully covered. The remaining gap is relational: nothing says how this tool relates to plan_company_data_migration/apply_company_data_migration or what preconditions (file existence, schema version) must hold.

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% and the sole parameter's schema entry is just a title and default, so the description must compensate — and it does, explaining both values of apply (false = dry run, true = perform the rename/fill/write). Nothing about the parameter's semantics is left ambiguous.

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?

States a specific verb and resource: 'Convert an existing employees.csv to the current column schema.' That is concrete enough to distinguish it from the CRUD siblings, but it never addresses the two very similarly named siblings plan_company_data_migration and apply_company_data_migration, so an agent still has to guess at the boundary between them.

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?

It clearly explains the operating mode: 'Runs as a dry run by default. With apply=True it renames...' which tells the agent exactly when the mutation actually happens. It stops short of naming alternatives (e.g. when to prefer plan_company_data_migration first), so there is context but no exclusions.

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 carries the full burden, and it does disclose the critical behavioral trait: 'Read-only: nothing is written.' It also enumerates what the report contains (valid fields, mappable fields, dropped data, open questions). It omits auth requirements and any limits, but for a zero-parameter analysis tool the read-only guarantee is the trait that matters most.

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?

Front-loads the core action in the first sentence, then qualifies with the read-only guarantee and the report contents. Three tight sentences, zero filler, nothing repeated.

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?

An output schema exists, so the description need not explain return values, and it correctly focuses on what the plan is based on and that it is non-mutating. The one gap is the lack of explicit positioning against the apply_company_data_migration sibling, which matters in a cluster of migration-related tools.

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 tool takes zero parameters, so there is no parameter surface to explain; the baseline of 4 applies. The implicit target files are named in prose, which is a small bonus but not a substitute for real parameter documentation.

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?

States a specific verb and resource: it proposes a field mapping between company_profile.json and financials.json, and clearly frames itself as a planning/analysis step. It does not, however, explicitly differentiate itself from the sibling apply_company_data_migration or inspect_company_data, leaving the agent to infer the routing.

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?

Usage is implied rather than stated: the mention of 'questions that must be answered before the data can be migrated' situates it as a pre-migration step, and it hints at sequencing before apply_company_data_migration. But it never names an alternative tool or states the explicit condition for choosing this over inspect_company_data.

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?

No annotations are provided, so the description carries the full burden. It discloses supported formats, which is useful, but says nothing about permissions, failure modes for unsupported types, size limits, encoding, or whether PDFs/DOCX are extracted versus read raw.

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?

A single front-loaded sentence with no filler; every word (operation, scope, supported formats, output type) 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 output schema exists, so return values need not be explained, and the tool is simple with one parameter. However, with no annotations and no schema descriptions, the description omits filename semantics and any safety/auth context, leaving a gap an agent would want filled.

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. Listing the supported extensions partially constrains the single 'filename' parameter, but it never states path format, case sensitivity, or base directory, so it only partly fills the gap.

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?

States a specific verb (Read) and resource (company TXT/MD/JSON/CSV/XLSX/PDF/DOCX file) and clarifies the output is text. It is distinguishable from list_company_files or inspect_company_data, though it never explicitly names a sibling to route the agent.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives such as list_company_files or inspect_company_data. Usage is only implied by the verb 'Read'.

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 carries the full behavioral burden. It implies upsert semantics (existing note replaced based on title) but never states what happens to an existing note, whether write permission is required, or whether the operation is destructive. This is a significant gap for a mutation 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?

A single efficient sentence with the action front-loaded. Nothing is wasted, though it is arguably under-specified rather than over-verbose.

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?

An output schema exists so return values need not be described, but for a 2-required-parameter mutation tool with zero annotation coverage and zero schema descriptions, the description leaves the agent without enough information to invoke it confidently.

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% for both required parameters, so the description must compensate. It hints that note_title acts as the identity key for the create-or-update behavior, but it says nothing about the content parameter, its length limits, or accepted formats.

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 gives a clear verb pair (create or update) and a specific resource (company notes) with enumerated note categories (policy, meeting, strategy, vision). It does not distinguish itself from siblings such as add_financial_record or add_employee, but the resource is 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?

There is no guidance on when to use this versus other company-data tools, nor any statement of prerequisites, ownership, or scope. The only usage hint is the implicit upsert implied by 'create or update', which is not elaborated.

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