FinanceGenius.AI

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

Annotations already cover the safety profile (readOnlyHint true, destructive false), so the description only needs to add behavioral context. It adds parameter meaning but does not disclose formula assumptions, edge cases, or output form; it does not contradict the annotations.

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 one focused purpose sentence followed by a compact Args list. The essential fact is front-loaded, and each argument line earns its place by supplying the units that the schema lacks.

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-only calculation with three documented inputs, the description is sufficient for an agent to select and invoke it. The only notable omission is that there is no output schema and the description does not state whether the result is returned as a decimal or percentage.

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 schema has 0% description coverage, so the description is the sole semantic source for parameters. It defines initial_value as 'Initial investment in rupees', final_value as 'Final value in rupees', and years as 'Holding period in years', covering every required parameter with meaningful units.

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 names a specific verb and resource: 'Calculate the Compound Annual Growth Rate (CAGR) of an investment.' This is clear and identifiable against siblings like calculate_sip or calculate_emi, but it never explicitly contrasts itself with those alternatives.

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 by the tool's purpose—if an agent needs an annualized growth rate from initial, final, and years, this is the tool. There is no explicit guidance about when to prefer it over related calculators, and no exclusion criteria.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is known to be a safe, non-mutating operation. The description adds valuable behavioral context beyond annotations by specifying the return payload: monthly EMI, total interest, total payment, and a year-by-year amortisation schedule. This clarifies the calculation scope and output shape that the agent would otherwise need to discover at runtime.

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 compact and well-organized with a one-line summary plus structured Args/Returns sections. Every sentence earns its place: the purpose is front-loaded, parameters are documented with examples, and return values are listed without padding. There is no redundant or vague prose.

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-only calculation tool with three parameters and no output schema, the description is fully self-contained. It explains what the tool does, the required inputs, and exactly what will be returned. The lack of an output schema is compensated by the explicit Returns section. No critical operational detail is missing for correct usage.

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 0%, so the description carries full responsibility for parameter meaning. The Args section explicitly documents all three parameters: principal as an amount in rupees, annual_rate with a concrete example of percentage notation, and tenure_months as loan tenure. This goes well beyond the bare schema, providing units, example, and format that are essential for correct invocation.

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 uses a specific verb and resource: 'Calculate EMI for a loan with year-by-year amortisation schedule.' This clearly distinguishes the tool from siblings like calculate_sip, calculate_cagr, and calculate_fd_maturity by naming the exact financial computation and adding the amortization detail. An agent can immediately understand what this tool does and that it is loan-specific.

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 loan EMI calculations but does not explicitly state when to prefer this tool over alternatives or when not to use it. Sibling tools cover other financial calculations (SIP, CAGR, FD), so an agent might infer the right choice from the tool name and description, but no direct routing guidance is provided. This is clear context without exclusions, so it falls at the 'implied usage' level.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds useful domain behavior through the compounding options and the note that quarterly compounding is the Indian FD norm, but does not disclose return format or edge cases. This is adequate given the simple, side-effect-free nature of the 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?

The description is a compact, well-structured docstring with a clear one-line summary followed by a concise Args section. Every line adds valuable information with no redundancy or fluff.

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 pure calculation tool with no output schema and four parameters, the description covers all inputs, their types, units, and domain conventions. The return value (maturity amount) is directly implied by the purpose statement, and the read-only annotations cover side-effect concerns. An agent has enough information to invoke this tool 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 0%, so the description must carry parameter meaning, and it does. It explains each parameter with units (rupees, percentage), a format example (1.5 for 18 months), and the allowed values plus default for compounding. This fully compensates for the missing schema 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 states a specific verb ('Calculate'), the resource ('FD maturity amount'), and the key mechanism ('with compounding'). It is clearly distinguishable from sibling calculation tools like calculate_emi or calculate_cagr, which target different financial products.

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 its use case through its name and explicit calculation purpose, and it adds practical context ('Most Indian FDs compound quarterly'). However, it does not explicitly state when to prefer this tool over alternatives such as find_best_fd or calculate_sip, nor does it mention 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?

Annotations already mark the tool as read-only and non-destructive. The description adds useful behavioral context by specifying the return payload — slab breakdown, total tax, effective rate, and regime comparison — and by documenting regime-specific deduction rules. No contradiction with annotations 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?

The description is compact and well-structured: a one-line purpose statement followed by an Args/Returns block. There is no filler, and the key scoping information (FY, regimes, deduction limits) is 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?

For a five-parameter tool with no output schema and a nuanced tax domain, the description is complete enough for correct invocation. It documents all parameter semantics, regime restrictions, and the expected return contents. Additional internal details like surcharge or slab rates are not required to call the tool successfully.

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 carries the full burden for parameter meaning. It explains each parameter, including that annual_income is gross income in rupees, regime defaults to 'new', 80C has a ₹1,50,000 cap, and 80C/80D/HRA apply only to the old regime. This is exactly what an agent needs beyond the bare schema titles.

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 pairs a specific verb ('Compute') with a precise resource ('Indian income tax for FY 2025-26') and names both tax regimes. This makes the tool's purpose unmistakable and clearly distinct from sibling calculators like calculate_emi or calculate_cagr.

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 first line establishes the target use case, and the Args section gives practical selection guidance: deductions and HRA are applicable only under the old regime, while the new regime is the default. It does not explicitly name alternatives, but the tax-specific context and sibling names make the intended usage clear.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds the purpose but omits relevant behavioral assumptions such as whether compounding is monthly and whether tax or inflation effects are included. For a calculator this is a moderate gap, not a contradiction.

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 compact and front-loaded: one purpose sentence followed by a concise per-parameter explanation. There is no filler, repetition, or unnecessary detail, and every sentence 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?

All three required parameters are clearly documented and the core operation is stated, but there is no output schema and the description does not specify the output units or return format. It also does not state compounding assumptions, which are important for a SIP calculation.

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 carries the full burden. It provides meaningful semantics for every parameter: monthly contribution in rupees, annual return as a percentage with an example, and investment horizon in years. This goes far beyond the bare schema property names.

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 'Calculate future value of a Systematic Investment Plan (SIP),' a specific verb and resource. This clearly distinguishes it from sibling tools like calculate_cagr, calculate_emi, and calculate_fd_maturity, which target different financial calculations.

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 tool versus alternatives such as calculate_cagr or calculate_emi. The description only documents parameters and leaves the agent to infer appropriate usage from the tool name and financial context.

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?

Annotations already signal the operation is read-only and non-destructive. The description goes beyond by specifying the output as a markdown table highlighting feature differences and by establishing the same-category constraint, giving the agent useful behavioral expectations without contradicting the annotations.

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 and well-structured with the primary purpose front-loaded, followed by a clear Args section and a Returns section. Every sentence earns its place with no irrelevant 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?

For a tool with a single parameter and no output schema, the description covers all essential aspects: what the tool does, the parameter constraint, and the return format. There is no missing information an agent would need to invoke it 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?

The input schema provides only the type (array of strings) with no description, so the description must compensate. It does so thoroughly by explaining the parameter represents product slugs, the allowed count range (2-4), the same-category requirement, and concrete examples (credit cards, FDs).

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 ('compare'), a clear resource ('products'), and the scope ('2-4 products of the same category'), which distinguishes it from single-product detail tools like get_product_details and from search or recommendation tools among the siblings.

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 clearly communicates the context: use it for side-by-side comparison of 2-4 products that must belong to the same category. It provides examples but does not explicitly name alternatives or state when not to use it, so it stops short of a full usage guideline.

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?

Annotations already mark the tool read-only and non-destructive. The description adds meaningful behavioral detail: it searches across brochures, regulatory guides, calculator help, and a live product database using BM25, semantic search, and product DB search with Reciprocal Rank Fusion. This tells the agent the tool aggregates multiple sources rather than returning a single static answer.

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 organized, front-loads the purpose, and includes useful examples and parameter documentation. The only slight inefficiency is the detailed search-algorithm explanation, which is informative but not strictly necessary for invoking the tool correctly.

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 read-only question-answering tool with two simple parameters, the description covers purpose, examples, and parameter semantics. It does not describe the return format or whether results include citations, but with no output schema and a straightforward answering tool, the description is sufficiently complete for correct invocation.

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 0%, so the description carries the full burden for parameters. It provides clear semantics for both: 'query: The financial question or concept to explain' and 'institution: Optional — narrow search to one institution (e.g. 'hdfc-bank')'. This goes well beyond the bare parameter names 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 opens with a specific action—'Answer financial questions using FinMall.AI's hybrid knowledge base'—and provides concrete example queries ('what is a critical illness rider?', 'difference between ELSS and PPF?'). It clearly positions the tool as a conceptual explainer, which differentiates it from calculator, product lookup, and comparison siblings.

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 states this tool is 'Best for explainers' and gives representative examples covering definitions, calculations, and comparisons. It conveys when to use it, though it does not explicitly name sibling tools to avoid or say 'use search_indian_products instead for raw product searches.' The context is clear enough for an agent to route appropriately.

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?

Annotations already establish the operation is read-only and non-destructive. The description adds meaningful behavioral context: results are limited to top 5, the cashback/rewards value is estimated, annual income is used as an eligibility filter, and prefer_no_annual_fee changes prioritisation. No contradictions with annotations are present.

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 compact and structured with a one-sentence summary followed by peer Arg/Returns lines. Every line adds information, and no unnecessary boilerplate or repetition 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 tool is simple, annotations confirm safety, and the description covers inputs and the high-level return shape despite lacking an output schema. It could be slightly more precise about the exact fields inside each returned card and whether all monetary amounts are INR, but nothing critical is missing for calling it 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?

With 0% schema description coverage, this description carries the entire burden for parameters and succeeds. It defines monthly_spend with units (rupees), enumerates all valid primary_category values, explains annual_income as an optional eligibility filter, and specifies that prefer_no_annual_fee prioritises lifetime-free cards.

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 uses a specific verb ('Recommend') and a precise resource ('Indian credit cards'), and adds the basis ('spending pattern'). It also states the output ('Top 5 cards with estimated annual cashback/rewards value'), which clearly separates it from sibling tools like compare_products or find_best_fd.

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 the use case — when a user wants Indian credit-card recommendations based on monthly spend and category — but it never explicitly states when to prefer this tool over siblings such as compare_products or get_product_details, nor gives exclusion conditions. Context is present but alternatives are not named.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context: it ranks by net-of-tax yield, explains that FD interest is fully taxable, and affects ranking via tax_slab_pct. It also states the output format (Top 5, gross & net amount). This goes beyond the annotations and clarifies the computation logic.

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 structured into a one-line purpose, Args, and Returns, making it scannable. The purpose is front-loaded. The Args section is necessary given the schema gap, so it earns its place. Slightly verbose but justified; 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?

Despite no output schema, the description clearly specifies the return: top 5 FDs with gross and net amounts. It covers the tax logic and the senior-citizen rate nuance. It does not mention any prerequisites or edge cases (e.g., minimum amount), but for a read-only ranking tool, this is sufficient. The description is complete enough for an agent to invoke it 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 0%, so the description bears full responsibility for parameter documentation. The Args section explains each parameter with units, examples ('1.5 for 18 months'), valid values (5/10/20/30), and the meaning of is_senior_citizen. This is exemplary compensation for the missing schema 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 a specific verb ('Rank'), a specific resource ('Indian Fixed Deposits'), and the ranking criterion ('net-of-tax yield'). This distinguishes it from siblings like calculate_fd_maturity, which presumably handles a single FD, and compare_products, which may be generic. The purpose is unambiguous and actionable.

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 explains what the tool does but does not explicitly contrast it with alternatives. It does not state 'Use this when comparing multiple FDs' or 'For a single FD calculation, use calculate_fd_maturity instead.' The context implies ranking across FDs, but the exclusion is not spelled out. Without that, an agent might confuse it with calculate_fd_maturity.

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?

Annotations already mark it readOnlyHint=true and destructiveHint=false, so no safety contradiction exists. The description adds behavioral value by listing the return fields (scheme name, latest NAV, NAV date, fund house, scheme type) and clarifying that the input is an AMFI scheme code. It does not cover failure modes, but for a simple read-only tool this is sufficient.

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 front-loaded with a single-sentence summary, followed by standard Args/Returns docstring sections. Every section earns its place: the example and returns list are useful, and the external references are concise. It is slightly verbose due to docstring formatting, but still well-structured and readable.

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 output schema, the description compensates by explicitly listing the return fields. Input requirements are covered by the required parameter in schema plus an explanatory Arg section with an example. For a simple one-param read-only tool, this is complete enough; missing error behavior is minor.

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 for the sole parameter 'amfi_scheme_code'—and it does. It provides a concrete example ('119598' for SBI Bluechip Direct) and directs users to external sources for finding codes. This adds real meaning beyond the bare 'string' type 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 states a specific verb ('Get') and resource ('latest NAV of an Indian mutual fund by AMFI scheme code'), clearly distinguishing it from sibling calculator and product tools. The one-sentence summary is unambiguous and leaves no doubt 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 makes the context clear: use this when you need the latest NAV for a given AMFI scheme code. It also provides guidance on finding codes via mfapi.in or amfiindia.com, though it does not explicitly name alternatives or exclusions. This is clear context with no exclusions, matching the '4' level.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that the tool retrieves 'full details' and relies on a URL-safe slug, but it does not disclose return shape, pagination, or other behavioral nuances. With annotations present, this is adequate but not rich.

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 compact and front-loaded with the core purpose. The parameter note is structured clearly and every sentence adds value, including the practical pointer to search_indian_products.

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 single-parameter read-only tool with no output schema, the description covers everything needed to call it: what it does, how the parameter should look, and where to find valid values. The absence of output schema is mitigated by 'full details' being sufficiently self-explanatory.

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 fully compensates. It explains that product_slug is a URL-safe identifier, provides a concrete example, and tells the agent where to obtain valid slugs. This gives enough meaning for correct invocation.

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 and resource: 'Get full details of a specific product by its slug.' It clearly distinguishes this tool from search_indian_products by indicating this is the follow-up lookup on an already-identified product, not the search itself.

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 clear context for when to use the tool: after obtaining a slug from search_indian_products. It does not explicitly list exclusions or compare against other siblings like compare_products, but the workflow hint is enough to guide an agent.

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?

Annotations only mark it read-only; the description adds that results are capped at 10 and sorted by popularity. It also specifies matching semantics: free-text matches name and description, and min_rating is a >= filter. This provides useful behavioral context beyond annotations.

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 with Args and Returns sections and is front-loaded with the primary purpose. Every line adds value, including concrete examples, with no filler or redundancy.

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 output schema, the Returns line explains output cap and ordering. Given the simple optional-parameter interface and read-only annotations, the description covers everything an agent needs to select and invoke this tool 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 0%, so the description fully compensates by documenting all three parameters. product_type lists all valid values and omission behavior, search_query defines match scope with examples, and min_rating gives the comparison and scale.

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 opens with a specific verb+resource statement: 'Search FinMall.AI's catalog of Indian financial products.' This clearly identifies what the tool does and distinguishes it from calculate, compare, explain, and get-details siblings. It is not a tautology because it adds the FinMall.AI catalog scope.

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 about when to use this search tool versus siblings like get_product_details or compare_products. The only usage-related note, 'Omit to search across all categories,' addresses parameter behavior rather than tool selection.

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