TGGET Niche Research

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

Annotations are empty, so the description carries the full burden, and it delivers a lot: recurring monthly execution, a digest notification, the fact that monthly checks do not consume researches, quota limits per plan, and idempotent behavior on repeat calls ("returns its project"). It still omits the initial cost/credit implication of the first follow and any permission or reversibility notes, so it is strong but not exhaustive.

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?

Information-dense and front-loaded with the action, then mechanism, then limits and idempotency. Each sentence carries distinct facts (plan quotas, no research spend, repeat behavior), though the single long first sentence could be split for readability.

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 one parameter, no annotations, and no output schema, the description covers preconditions, side effects, recurrence, notification behavior, quotas, and idempotency. The only gap is what the call returns for a brand-new follow (the return value is described only for the already-followed case).

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 the baseline is 3, but the description adds a real constraint beyond the schema: the referenced scan must be finished and must have a report. That eligibility nuance meaningfully refines how the single research_id argument should be chosen.

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 ("Starts following the niche of a finished scan") and then explains the resulting mechanism: the scan becomes the first measurement, monthly re-scans occur, and a change digest is emailed. An agent can distinguish this from siblings like run_signal, start_research, or get_report 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 a clear precondition ("a finished scan that has a report") and a plan-capacity constraint (FREE 1, Starter 2, Pro 10), plus idempotency behavior when already followed. It does not explicitly name a sibling alternative or a when-not-to-use condition, so it stops just short of the top band.

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 readOnlyHint already covering the safety profile, the description still adds real context: results are newest-first, the baseline is the first measurement, each check carries what changed since the previous check, and changes are computed deterministically rather than by an LLM. This is substantive disclosure beyond 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?

Well front-loaded: the return shape leads, the downstream should not be the first focus, and each sentence carries information. Slightly dense with backticked field names but no wasted sentences.

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?

No output schema exists, so the description must describe return values, and it does so clearly (baseline, monthly checks, numbers, deltas, research_id). It is complete enough to call the tool correctly, with only the sibling-differentiation gap remaining.

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 there is one parameter, whose provenance is already documented in the schema ('Project id listed by list_projects'). The description adds no further meaning to the id argument, so the baseline of 3 for high coverage 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 specific verb (returns) and resource (a followed niche with its history of checks), and details the shape of the payload: baseline plus monthly checks, with numbers and changes. It implicitly distinguishes itself from get_report by routing the agent there for the fresh report, but does not explicitly contrast with siblings like get_research or list_projects.

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 gives a useful chain hint ('Pass that research_id to get_report'), which implies this tool is the entry point for reading a project's check history. However, it never states when to use get_project versus get_research, list_projects, or get_report directly, so the routing guidance is only inferred.

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 does disclose the returned content (sections, Markdown default, JSON structure), which is genuinely useful, but says nothing about permission/auth needs, behavior when the scan is unfinished or the id is unknown, or whether an older report revision is returned (only that it is the 'latest').

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 purpose is front-loaded in the first clause, which is good, but the rest is a single sprawling sentence enumerating every report subsection. Much of that enumeration is informative, yet the wall-of-text structure hurts scannability and duplicates detail the report itself will supply.

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?

No output schema exists, so the description must describe the return value — and it does so thoroughly, including the Markdown vs. JSON distinction and the JSON keys. The remaining gap is failure/edge-case behavior (unfinished scan, invalid id), which keeps it short of complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, and the description goes further by spelling out the JSON key set (verdict, product, economics, seo, aso, names, landing, channels, validation, risks) and clarifying that 'id' accepts either a scan id or a report id, adding meaning beyond the schema's short field notes.

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+resource (returns the final niche report of a finished scan) and enumerates the report's sections, so the agent knows exactly what it gets. It does not, however, explicitly distinguish itself from close siblings like get_research or compare_research, which also return research artifacts.

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 only implied: the phrase 'of a finished scan' hints that the scan must be complete, and the format note implies reading vs. programmatic consumption. There is no explicit statement of when to pick this over get_research, nor any exclusion or prerequisite guidance.

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 burden and does disclose a meaningful behavioral trait: the resource is stateful and may be incomplete, so the tool must be polled until status is completed and pending is 0. It does not cover auth, error cases, or rate limits, leaving some gaps for an unannotated 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?

Front-loaded with the verb and resource, and the second sentence is a tight polling instruction. The field enumeration is dense but each item (child researches with ids, skipped stages) is load-bearing information for a tool with no output schema.

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 and a complex nested return, the description does the necessary work of naming the returned fields, including child researches and their ids. It does not define exactly what 'pending' counts or how status values map, so it is strong but not exhaustive.

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 the single param description already explains the id's provenance ('returned by start_research or listed by list_research'). The description only restates 'by id' and adds nothing beyond the schema, so the 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?

States a specific verb (returns) and resource (research by id) and then enumerates the payload — status, headline numbers, ranked candidate phrases, competitor overview, child researches, skipped stages. This is clearly distinguishable from list_research (which enumerates rather than fetches one) 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 an explicit usage pattern: 'Call repeatedly until status is completed and pending is 0,' which tells the agent this is a polling tool with a termination condition. It does not name alternatives (e.g., list_research to discover ids, get_report for the final artifact), so it stops short of full when/when-not guidance.

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 adds useful sorting behavior ('newest first') and return fields, but omits auth/permission requirements, pagination behavior beyond a count limit, and any statement of read-only safety.

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 parenthetical defining 'researches' is the only slightly clunky element but does useful work.

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 list tool with no output schema and no annotations, the description covers what is returned (status, headline demand) and ordering. Auth and pagination details are the only notable gaps.

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% for the single limit parameter with its default and range already documented. The description adds nothing about the parameter, so the 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?

States a specific verb (Lists) and resource (top-level researches) and adds the return shape (status, headline demand) plus sort order. It implicitly separates itself from get_research by saying 'top-level' and 'list', but never names the sibling for single-item retrieval.

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 and no condition selecting it over get_research, compare_research, or follow_niche. The list semantics are implied by the verb but alternatives are never addressed.

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 burden and does well: it discloses that this is a quota-consuming job (one research, or two with deep), that cache-served data still counts against allowance, and that it returns an id to poll, implying asynchronous execution. It doesn't cover failure modes or how long a job takes, which keeps it short of a 5.

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 sentences, front-loaded with what the tool does and how each input form is handled, then the returned id, then cost. Every sentence carries information an agent needs; no padding.

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?

No output schema exists, and the description compensates by saying a research id is returned for polling with get_research. Quota and deep-mode availability are covered, but async timing and error behavior are not, leaving a small gap for a job-starting 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 coverage is 100%, so the baseline is 3, but the description adds real meaning: it explains that `text` is accepted either as a raw phrase or as a description that gets converted into search queries, and that `deep` costs double. It adds no extra detail on `locale` beyond 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?

States a specific verb and resource ('Starts a niche research') and distinguishes two distinct input modes (phrase vs. plain-language description) with what each does. It also names the follow-up sibling (get_research) the agent needs to poll with, so the tool is unambiguous against its 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?

Clear usage context: pass a phrase for a direct scan, pass a description when the idea needs to be turned into queries, and poll results with get_research afterward. It does not, however, say when to prefer this over compare_research or follow_niche, so no explicit exclusions are given.

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