Verified data on 8,000+ AI tools for any AI assistant: live status, pricing reality, user sentiment, viability, and alternatives, every answer carries a last-verified date. Free, read-only, no auth.
Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own score and last-verified date. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | The category, in plain words or as a slug — e.g. "image generation", "code-development". | |
| response_format | No | concise = size, distribution and the strongest few. detailed = adds the weakest end and the decay rate. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even with readOnlyHint=true annotations, it adds meaningful behavioral context: exact return components (tool count, survival-band distribution, decay rate, strongest/weakest examples with scores and last-verified dates) and non-obvious semantics (overlapping categories, risk vs quality, no company-size notion). No contradiction with 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?
Every sentence earns its place: usage, examples, return composition, domain caveats, and exclusions. It is long but dense and front-loaded with the most decision-relevant 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?
There is no output schema, so the description rightly explains what is returned. It also covers caveats needed for correct interpretation and names alternatives. Together with full schema coverage, an agent can select and call 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 100%: category_name's exact-match behavior and format's concise/detailed meaning are already documented. The description adds useful background about category overlap but does not need to repeat parameter-level details.
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 + resource (analyze the landscape of a whole AI tool category) and lists concrete questions it answers. It also explicitly distinguishes itself from compare_tools, recommend_tools, and deadpool_digest, so an agent can select it without confuseion.
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 opens with direct when-to-use guidance ('Use this when the user asks what a whole category...'), provides two example prompts, and closes with an explicit 'Not for' list naming the correct alternative tools. This is exceptionally clear routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks whether a specific AI tool is alive, dead, shut down, still maintained, safe to adopt, or trustworthy — or asks for its current health, viability, or verification status. Returns a verified verdict (healthy / monitor / at-risk / shut down / delisted) with evidence: link-health probe results, a 5-signal viability assessment, real-user market sentiment, pricing reality, and verified-alive alternatives. Data comes from the RightAIChoice verification engine: 8,000+ AI tools with every vendor link re-probed on a rolling weekly cycle. Every answer states when it was last verified. Not for: tools outside the AI/software space, historical company research, or legal/financial advice. An "unknown" result means the tool is not in the catalog — it is not evidence the tool is dead.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | The AI tool to check — product name (e.g. "Jasper") or site slug (e.g. "jasper"). Pass an ARRAY of up to 20 names to assess a whole stack at once. | |
| response_format | No | concise = verdict + freshness + source link. detailed = adds viability signals, link health, sentiment, pricing, and alternatives. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even though annotations already mark the tool as read-only and non-destructive, the description adds substantial behavioral context: it returns a verified verdict with link-health probes, a 5-signal viability assessment, sentiment, pricing, and alternatives; data comes from an 8,000-tool engine with weekly re-probing; and every answer states its verification time. The 'unknown means not in catalog' caveat is especially important and aligns with the closed-world annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the trigger case and then flows logically through output, evidence, data source, freshness, exclusions, and the unknown-result caveat. Every sentence earns its place and there is no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter read-only tool with no output schema, the description is complete: it names the verdict values, evidence components, verification freshness, exclusions, and the meaning of 'unknown'. An agent can determine when to use it and what to expect without needing additional documentation.
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 input schema already explains both parameters well: the tool parameter accepts a name or slug and supports arrays up to 20, while response_format has a clear concise/detailed enum. The main description mentions the evidence categories but does not add meaningful parameter-level meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific trigger ('asks whether a specific AI tool is alive, dead, shut down, still maintained, safe to adopt, or trustworthy') and lists the verdict types returned, making the core function concrete. It also excludes out-of-scope requests such as historical company research and legal/financial advice. However, it never explicitly contrasts sibling tools like viability_score, market_sentiment, or pricing_reality, which overlap with parts of its evidence bundle.
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 explicit 'Use this when...' and 'Not for...' guidance, so an agent knows when to invoke it and what to avoid. It also clarifies what an 'unknown' result means, preventing a common misinterpretation. Yet it does not name an alternative tool for the excluded cases, so the when-versus-alternative distinction is not fully spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks how two specific AI tools compare, which of two tools to choose, or "X vs Y". Returns live verification status for BOTH tools side by side (alive/at-risk/shut-down verdict, last-verified date, pricing reality, market sentiment) plus an editorial head-to-head verdict when a reviewed comparison exists for the pair. Data comes from the RightAIChoice verification engine: 8,000+ AI tools with every vendor link re-probed on a rolling weekly cycle, and 19,000+ maintained comparison analyses. Not for: single-tool checks (use check_tool_status), comparing more than two tools, or category browsing. If either tool is reported as not in the catalog, that is not evidence it is dead.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_a | Yes | First AI tool — product name (e.g. "Jasper") or site slug (e.g. "jasper"). | |
| tool_b | Yes | Second AI tool — product name or site slug. | |
| response_format | No | concise = per-tool verdict + pricing + editorial verdict. detailed = adds sentiment, viability signals, and link health per tool. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Goes well beyond the read-only annotations by disclosing exactly what is returned: side-by-side live verification status, last-verified dates, pricing reality, market sentiment, and a conditional editorial verdict. It also explains the data source cadence and warns that a 'not in catalog' result is not evidence a tool is dead.
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 the usage trigger, then covers outputs, data provenance, exclusions, and a caveat in a compact, logical order. Every sentence contributes useful information without fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description sufficiently explains return values and behaviors: the per-tool verdict, side-by-side nature, conditional editorial verdict, and the important caveat about catalog absence. It fully equips an agent to invoke the 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?
The input schema already describes all three parameters with clear formats and enum values, and coverage is 100%, so the description does not need to repeat parameter details. It adds some context about what response_format might affect, but the schema carries the main semantic load.
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: compare exactly two specific AI tools head-to-head, with explicit trigger phrases ('X vs Y', 'which of two tools to choose'). It clearly distinguishes from siblings by emphasizing the two-tool scope and naming alternatives like check_tool_status.
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?
Provides explicit when-to-use guidance (two-tool comparisons) and explicit exclusions: single-tool checks, comparing more than two tools, and category browsing. It directly names the sibling alternative check_tool_status for single-tool use, which is strong guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks how many AI tools die, AI startup failure or shutdown rates, which AI categories decay fastest, how risky the AI tool market is, or for data behind "most AI tools fail" claims. Returns two clearly separated datasets: (1) LIVE catalog decay — 8,000+ verified AI tools with broken vendor-link rates, dead-homepage counts, pricing opacity, and the fastest/slowest-decaying categories, updated daily; (2) a FROZEN dated survival study of 2,291 top Product Hunt launches (of the 2,066 with a determinable outcome, 24.4% are dead within ~2 years). Every figure carries its as-of date. Data comes from the RightAIChoice verification engine; link-decay figures count only VENDOR-published links (site/docs/changelog/repo), never our own derived URLs. Not for: checking one specific tool (use check_tool_status) or predicting a specific tool's future (use viability_score). Decay rates describe categories and cohorts, not individual products.
| Name | Required | Description | Default |
|---|---|---|---|
| response_format | No | concise = headline mortality figures from both datasets. detailed = adds fastest/slowest-decaying categories and survival splits by launch votes and rank. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly=true, destructive=false), the description discloses methodology: it separates LIVE catalog decay from FROZEN dated survival study, counts only vendor-published links, and updates daily. This adds significant behavioral context about the data source and processing.
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 long but well-structured, starting with usage, then datasets, then exclusions. Every sentence provides useful information without redundancy. It is slightly verbose for the sake of thoroughness, but earns its length.
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 complex tool with two datasets and no output schema, the description is complete: it explains both datasets, their update frequency, data sources, counting methodology, and limitations. The annotations cover safety, so no additional behavioral disclosure is needed.
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 one parameter (response_format) with 100% coverage including descriptions for both enum values. The tool description does not add any additional parameter semantics, so it relies on the schema, which fully covers it. 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?
The description clearly states what the tool does: it provides AI tool mortality statistics including failure rates, decaying categories, and survival studies. It explicitly distinguishes from sibling tools by noting it is not for checking one specific tool (check_tool_status) or predicting a specific tool's future (viability_score).
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 explicit use cases ('Use this when the user asks...') and exclusions ('Not for: checking one specific tool (use check_tool_status) or predicting a specific tool's future (use viability_score)'). It also clarifies that decay rates describe categories and cohorts, not individual products, which is a critical boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks what to use instead of a specific AI tool, for its alternatives or competitors, or for a replacement because the tool shut down, got acquired, raised prices, or is unreliable. Returns up to 3 verified-alive alternatives from the same category, each with a one-line description, current health verdict, last-verified date, and pricing model. If the asked-about tool shut down, says so (with the recorded reason) and returns live replacements. Alternatives come from the RightAIChoice verification engine (8,000+ AI tools, every vendor link re-probed on a rolling weekly cycle), ranked by category and identity-tag match — never filler from unrelated categories. The response states whether results are curated matches or a category roll-up. Not for: comparing two named tools (use compare_tools) or checking a single tool's status (use check_tool_status).
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | The AI tool to find alternatives for — product name (e.g. "Jasper") or site slug (e.g. "jasper"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description openly discloses return details (up to 3 alternatives with fields), shutdown behavior, ranking methodology, source ('RightAIChoice verification engine'), and the transparency about curated vs. roll-up results. This adds rich context beyond the readOnlyHint annotation, with no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every sentence adds value—use case triggers, output format, edge-case behavior, ranking credibility, and exclusions. It is well-organized, with a clear 'Not for' section that closes with alternatives.
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 fills in the gap by specifying exactly what the response contains (up to 3 alternatives, each with one-line description, health verdict, last-verified date, pricing model) and handles edge cases like shutdowns. For a tool with one parameter, this is 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?
The schema already fully documents the only parameter 'tool' with examples. The description adds no new parameter-level semantics, so the baseline of 3 applies. It does not repeat schema info, which is good, but offers no extra parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool finds alternatives to AI tools, listing specific trigger scenarios (shutdown, acquisition, price hikes, unreliability). It distinguishes itself from siblings by name-dropping compare_tools and check_tool_status as 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?
The description provides when-to-use guidance ('when the user asks what to use instead of a specific AI tool...') and explicit exclusions ('Not for: comparing two named tools...'). This fully satisfies the dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks what people actually think of a specific AI tool, whether users like it, its reputation, reviews, community feedback, common complaints, or praised strengths. Returns a synthesized sentiment report from real user discussions (Reddit, Hacker News, GitHub, YouTube, Product Hunt and more): overall sentiment label, per-source positivity, top pros and cons, recurring themes, and mention volume — always stamped with the scan date. Reports come from the RightAIChoice sentiment engine and are served from the verified cache (usability gate: completed scans under 180 days old). If no usable scan is on file, this tool says so honestly instead of guessing. SUBJECT GATE: where we hold a scan but cannot establish that the posts are about this product rather than something else sharing its name, NO score, count or breakdown is returned and the reply says so explicitly. That reply means "we checked and disproved the subject" — it is not an absence of data and not a negative signal about the product. Not for: checking whether a tool is alive (use check_tool_status) or head-to-head choices (use compare_tools). Sentiment reflects public discussion volume, not product quality rankings.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | The AI tool to get sentiment for — product name (e.g. "Jasper") or site slug (e.g. "jasper"). | |
| response_format | No | concise = label, verdict line, top pro/con, mention count. detailed = adds per-source breakdown, up to 4 pros/cons, and recurring themes. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only and non-destructive, and the description adds substantial behavioral context: reports come from a verified cache with a 180-day usability gate, the tool honestly reports no usable scan, and the SUBJECT GATE explains the meaning of a no-score reply. This goes well beyond the structured annotations and prevents misinterpretation of empty results.
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 long but front-loaded with the usage trigger and output summary, then covers cache behavior and gate semantics. Information is well organized, though the closing caveat about sentiment reflecting volume not quality is slightly redundant with the earlier 'Not for' sentence.
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?
There is no output schema, so the description must explain return content, which it does in detail (sentiment label, per-source positivity, pros/cons, themes, mention volume, scan date). It also covers failure modes and the distinction between no data and a disproved subject, making the tool fully understandable for an agent with no additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both 'tool' and 'response_format' already have detailed descriptions in the input schema. The description does not add extra parameter semantics beyond what the schema provides, 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?
The description opens with a precise use case ('what people actually think of a specific AI tool'), names the resource ('AI tool'), and specifies the exact output: a synthesized sentiment report with label, per-source positivity, pros/cons, themes, and mention volume. It explicitly distinguishes itself from compare_tools by saying it is 'Not for product quality rankings or head-to-head choices'.
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 clear 'Use this when' triggers (likes, reputation, reviews, community feedback, complaints) and an explicit 'Not for' exclusion that routes head-to-head comparisons to compare_tools. It also details internal gates (cache usability, subject gate) that determine when results are returned or not, so an agent knows exactly when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks what a specific AI tool costs, its pricing model, plans and tiers, whether it has a free tier, hidden costs, or whether the vendor even publishes prices. Returns the recorded pricing model, per-plan prices exactly as the vendor lists them, and — unique to this catalog — documented HIDDEN COSTS: annual-commitment markups, seat minimums, paywalled features, and undisclosed usage fees. Data comes from the RightAIChoice verification engine (8,000+ AI tools re-verified on a rolling weekly cycle). The response distinguishes "the vendor publishes no prices" (their choice — sales call required) from "not recorded in our catalog" (our gap), and never converts currencies. Not for: negotiating quotes, historical price tracking, or financial advice. Prices change frequently — treat the verification date as part of the answer.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | The AI tool to get pricing for — product name (e.g. "Jasper") or site slug (e.g. "jasper"). | |
| response_format | No | concise = pricing model + plan prices + top 2 hidden costs. detailed = full plan list with key features and up to 6 hidden costs. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and destructiveHint, but the description adds significant behavioral context: data source (RightAIChoice engine), re-verification frequency, differentiation between vendor-no-prices and catalog-gap, and currency non-conversion. Also notes 'Prices change frequently' and verification date as part of answer, going beyond what annotations provide.
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 lengthy but each sentence adds distinct value: hidden costs, data source, exclusions, and verification date. It is front-loaded with the use case and clearly structured. While not terse, it avoids fluff and maintains organization.
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 pricing tool with two parameters, no output schema, and rich annotations, the description provides complete context: return contents, hidden costs, distinction between no-prices and not-recorded, and verification date. It adequately covers user expectations and edge cases, making it self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, meeting the baseline, but the description adds meaning for response_format by explaining the exact differences (concise vs detailed outputs). The tool parameter is already descriptive in schema, but this extra detail for response_format elevates the score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb ('get', 'fetch') and resource ('pricing of AI tools'), clearly distinguishing itself from siblings by highlighting hidden costs and verification engine. It explicitly states 'Use this when the user asks what a specific AI tool costs' and scopes to pricing queries.
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?
Explicitly states when to use ('when the user asks what a specific AI tool costs') and provides clear exclusions ('Not for: negotiating quotes, historical price tracking, or financial advice'). Though it doesn't name alternative tools, the exclusions and context are sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user describes what they need an AI tool to DO rather than naming one — "something to transcribe interviews", "an image generator with an API", "a writing tool for a small team". Returns a verified shortlist matched on the described need, filtered by constraints the caller sets, and — unique to this catalog — screened against a survival bar: tools failing our link-health probes or scoring badly on the 5-signal viability model can be excluded outright. Each result carries its own health verdict, last-verified date and pricing model. Matching is keyword and fuzzy text search over 8,000+ verified tools; the response states this. Tools with no viability measurement yet are listed separately by name rather than silently dropped. Not for: questions about a tool the user already named (use check_tool_status), head-to-head choices (use compare_tools), or filtering by price amount — prices are recorded verbatim in mixed currencies and are never converted or compared numerically.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | What the tool must do, in the user's own words. e.g. "transcribe podcast interviews with speaker labels". | |
| limit | No | How many results to return (max 10). | |
| requires_api | No | Only return tools recorded as offering an API. | |
| survival_bar | No | exclude_at_risk = drop tools scoring below the at-risk threshold, with a failing main site, or whose published website now redirects to a different company. safe_bet_only = only the strongest band. none = no survival screening (moved-identity tools are then returned WITH the move stated). | exclude_at_risk |
| pricing_model | No | Filter by recorded pricing model. has_free_option = free or freemium. There is no price-amount filter: prices are verbatim strings in mixed currencies. | any |
| exclude_wrappers | No | Exclude tools assessed as thin wrappers around a third-party model. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare readOnly=true, but the description adds substantial behavior: link-health probes, 5-signal viability scoring, separate listing for unmeasured tools, moved-identity handling, and verbatim un-converted pricing. No hidden side effects or contradictions with 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 dense but purpose-built: it front-loads the call condition, packs constraints and result contents into the second sentence, and closes with a three-item 'Not for' list. There is no filler or repeated schema text.
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 six-parameter tool with no output schema, the description covers input trigger, filtering knobs' intent, return contents (health verdict, last-verified, pricing), limitations, and sibling routing. An agent has everything needed to invoke it correctly and set expectations about results.
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 goes beyond schema by clarifying survival_bar's moved-identity behavior, the no-price-amount constraint on pricing_model, and the keyword/fuzzy matching over 8,000+ tools. It doesn't need to repeat every parameter since the schema is 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?
The description opens with 'Use this when the user describes what they need an AI tool to DO rather than naming one' and specifies it returns a verified, survival-screened shortlist. This makes the tool's job and its distinguishing 'survival bar' factor clear next to siblings like check_tool_status and compare_tools.
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 explicitly states the triggering condition (user describes a need, not a named tool) and gives exclusions with exact alternatives: 'Not for... use check_tool_status', 'use compare_tools', and price-amount filtering. This is model-ready routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks whether a specific AI tool is safe to adopt or build on, likely to still exist next year, well-maintained, gaining or losing momentum — or asks about its long-term viability or abandonment risk. Returns a viability BAND (safe bet / moderate / at risk) with the five measured drivers behind it: what the vendor publishes, site health, traction, user sentiment, and recent activity — plus which signal is weakest and why, in plain language. Assessments come from the RightAIChoice verification engine (8,000+ AI tools, every vendor link re-probed on a rolling weekly cycle). A null driver means "not yet measured", which is different from a bad score. Bands are NOT a leaderboard: they classify risk, they do not rank tools against each other. Not for: comparing two tools (use compare_tools), liveness checks alone (use check_tool_status), or investment decisions.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | The AI tool to assess — product name (e.g. "Jasper") or site slug (e.g. "jasper"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false, so the safety profile is known. The description adds substantial behavioral context: it returns a viability band with five measured drivers, explains that null means 'not yet measured' (distinct from a bad score), and clarifies that bands are risk classifications, not rankings. It also mentions the rolling weekly re-probing of vendor links, giving transparency into data freshness.
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 information-dense but well-structured: use cases, return value, methodology, null semantics, and exclusions are each clearly separated. Every sentence adds a distinct piece of information – no fluff, no repetition of schema or annotations. It is appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description carries the burden of explaining return values. It clearly describes the output format (viability band, drivers, weakest signal) and interprets the 'null driver' case. It also notes the scale of the underlying engine (8,000+ tools) and the re-probing cycle. Given the tool's analytical nature, this is a complete and self-contained description.
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 already documents the single parameter 'tool' with a description that includes examples and field format. Maximum length and required status are also in the schema. Since schema description coverage is 100%, the baseline is 3. The tool description does not add further parameter-specific nuances beyond what the schema provides, so no higher score is warranted.
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 specific use cases: assessing safety to adopt, longevity, maintenance, momentum, and abandonment risk. It clearly distinguishes itself from siblings by naming compare_tools and check_tool_status as alternatives for other needs. The verb 'use this when' and concrete risk questions make the purpose unmistakable.
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?
Explicit 'when to use' scenarios are listed in the first sentence, followed by an explicit 'Not for' section that names two alternative tools. This directly answers when to use this tool versus alternatives, providing clear decision criteria for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this when the user asks what has changed recently in AI tools — whether a specific tool has changed its pricing, links, domain or health, or what has moved across the market since a given date. Returns dated, field-level changes we actually observed: vendor pricing-page edits, recorded price changes, link health moving, domain and certificate changes, and vendors moving their site. Each carries the date we observed it and how. Reports only VENDOR-OBSERVABLE changes by default — our own editorial rewrites are excluded and the response says so, because our writing changing is not the market moving. Our change record begins on a fixed date stated in every answer; asking about an earlier period returns that date rather than an empty result. Not for: predicting future changes, or a tool's current status (use check_tool_status).
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | Optional. A single AI tool — product name or slug. Omit for a market-wide summary. | |
| since | No | Optional ISO date (YYYY-MM-DD). Defaults to 7 days ago. Earlier than our coverage floor returns the floor. | |
| include | No | vendor_changes = only what the vendor did. vendor_and_our_assessment = also our survival re-scores. Our editorial rewrites are never included. | vendor_changes |
| response_format | No | concise = counts plus the most notable changes. detailed = the individual dated changes. | concise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavior beyond that: the fixed coverage floor date, the vendor-observable-only default, the exclusion of editorial changes, and the fact that each change carries an observation date and method. No contradiction with 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?
Although longer than average, every sentence earns its place: usage trigger, return format, exclusion policy, coverage floor, and alternatives. The most decision-critical information is front-loaded, and the 'Not for' clause cleanly closes ambiguity.
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, four-optional-parameter tool with no output schema, the description is complete. It explains what is returned, how data is observed, what is excluded, the coverage floor behavior, and when not to use it. The sibling name for current status closes the main routing gap.
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 all four parameters (tool, since, include, response_format) are already documented with defaults and enums in the schema. The description reinforces the conceptual model (e.g., coverage floor) but does not need to repeat parameter mechanics. 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?
The description clearly states the tool's specific purpose: reporting observed, dated changes in AI tools (pricing, links, domain, health) and market movement since a date. It names the resource (AI tool market changes) and distinguishes itself from check_tool_status by explicitly excluding current-status queries.
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 concrete when-to-use triggers ('when the user asks what has changed recently'), states what is excluded (editorial rewrites, future predictions), and explicitly routes current-status queries to check_tool_status. This is exemplary usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
recommend_tools1 field changedInput schema / properties / survival_bar / descriptionPrevious value: -"exclude_at_risk = drop tools scoring below the at-risk threshold or with a failing main site. safe_bet_only = only the strongest band. none = no survival screening."New value: +"exclude_at_risk = drop tools scoring below the at-risk threshold, with a failing main site, or whose published website now redirects to a different company. safe_bet_only = only the strongest band. none = no survival screening (moved-identity tools are then returned WITH the move stated)."category_landscapecheck_tool_status5 fields changedInput schema / properties / tool / anyOfAdded value: +[
+ {
+ "maxLength": 120,
+ "minLength": 1,
+ "type": "string"
+ },
+ {
+ "items": {
+ "maxLength": 120,
+ "minLength": 1,
+ "type": "string"
+ },
+ "maxItems": 20,
+ "minItems": 1,
+ "type": "array"
+ }
+]Input schema / properties / tool / descriptionPrevious value: -"The AI tool to check — product name (e.g. \"Jasper\") or site slug (e.g. \"jasper\"). One tool per call."New value: +"The AI tool to check — product name (e.g. \"Jasper\") or site slug (e.g. \"jasper\"). Pass an ARRAY of up to 20 names to assess a whole stack at once."Input schema / properties / tool / maxLengthRemoved value: -120Input schema / properties / tool / minLengthRemoved value: -1Input schema / properties / tool / typeRemoved value: -"string"recommend_toolswhats_changedcheck_tool_statuscompare_toolsdeadpool_digestfind_alternativesmarket_sentimentpricing_realityviability_scoreClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
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Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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Add one secure layer between your agents and this server.
Most tools split cleanly by question shape (single-tool vs pair vs category vs market), but check_tool_status overlaps heavily with viability_score, market_sentiment, and pricing_reality by bundling the same evidence into one verdict. The extensive not-for cross-references help an agent route, yet several boundary cases like 'is this safe to adopt?' could reasonably go to multiple tools.
The names are all readable lowercase snake_case, but they mix verb-led names (check_tool_status, compare_tools, find_alternatives, recommend_tools) with noun-phrase names (category_landscape, market_sentiment, pricing_reality, viability_score), and whats_changed follows neither pattern. There is no single consistent verb_noun convention, though the style is still predictable enough to be browsable.
Ten tools is an appropriate size for a domain covering single-tool research, pairwise comparison, recommendations, alternatives, and aggregate market statistics. Each tool has a reasonably narrow slice of responsibility, so none feel redundant or like filler.
The set covers the key query lifecycle for AI-tool intelligence: liveness, viability, sentiment, pricing, change history, comparisons, alternatives, recommendations, category health, and market mortality. Cross-references between tools prevent dead ends, and the only notable limitations are explicitly disclosed rather than silently missing.