Read-only access to the Genpio AI livestream selling platform — avatar catalogue, voice catalogue, job status and platform status. No account or API key required.
Explain what Genpio is and point to the machine-readable documents that answer product, pricing and FAQ questions in depth. Hands off rather than guessing: a commercial question is routed to a human instead of answered.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds valuable behavioral context by stating that the tool 'points to machine-readable documents' and that it will route commercial questions to a human instead of guessing, which is not derivable from 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 two sentences with no redundancy. The first sentence front-loads the core purpose, and the second adds a crucial behavioral nuance. Every word earns its place, making it highly concise and well-structured.
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 zero-parameter, no-output-schema tool, the description is complete. It explains what the tool does, where it points, and how it handles commercial questions. Given the tool's simplicity and the rich annotations, no additional context 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 tool has zero parameters, and the schema coverage is trivially 100%. With no parameters to document, the description doesn't need to add parameter semantics; the baseline for 0-param tools is 4, and the description is consistent with this.
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 function with a specific verb ('Explain') and resource ('what Genpio is'), and further specifies that it points to machine-readable documents for product, pricing, and FAQ questions. This distinguishes it from sibling tools like list_avatars or service_status, which are purely list/status 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?
The description gives clear context on when to use this tool (for an overview and document pointers) and provides behavioral guidance: commercial questions should be routed to a human rather than answered. It doesn't explicitly name alternative tools, but the 'hands off rather than guessing' instruction effectively communicates a boundary for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report how many public avatars Genpio publishes and return their ids, along with the URL template that renders a portrait. Every avatar is driven by Genpio's own in-house avatar model on Genpio's proprietary technology, not a licensed or third-party avatar engine. The library carries no per-avatar metadata, so this cannot filter by name, gender or nationality. Private customer avatar clones are never listed.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many avatar ids to return. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations already declare readOnlyHint and idempotentHint, the description adds significant behavioral context: avatars are generated by Genpio's proprietary model (not third-party), there is no per-avatar metadata, and private clones are excluded. These details go beyond the annotations and clarify scope and limitations.
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 three sentences long, front-loaded with the primary purpose in the first sentence, followed by two sentences of relevant context. Every sentence earns its place without 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?
Given the tool's simplicity (one optional parameter, no output schema), the description provides a complete picture: what is returned (count, ids, URL template), the proprietary nature, the lack of filtering, and the exclusion of private avatars. This is sufficient for an agent to decide whether to use the tool and what to expect.
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 single 'limit' parameter with a clear description. The tool description does not add any additional meaning about the parameter itself, but the schema coverage is 100%, so the baseline of 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 function: report how many public avatars Genpio publishes, return their ids, and provide the URL template for portraits. This is a specific verb+resource+output, and the mention of 'public avatars' and 'private clones never listed' distinguishes it from sibling tools like list_voices or list_open_roles.
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 this tool, including explicit exclusions: it cannot filter by name, gender, or nationality because no per-avatar metadata exists, and private customer avatar clones are never listed. However, it does not explicitly name alternative tools for those cases, so it stops short of full when/when-not guidance with named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the roles Genpio is currently hiring for, with title, department, location, type, summary and posting date, plus links to the full Markdown job description and the human-facing careers page.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many roles to return. | |
| query | No | Optional filter matched against title, department, location and type. Omit to list every open role. |
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 a live-scope nuance ('currently hiring for') and describes the return content (fields and links), but doesn't disclose additional behavioral traits like authentication needs or rate limits. Credit is given for the scope constraint, but it remains within the baseline.
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 single, front-loaded sentence that immediately states the tool's purpose, then efficiently enumerates the returned fields and links. Every clause adds value and there is no redundant or filler content.
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 listing tool with two well-described parameters and no output schema, the description adequately covers the returned fields and the fact that it's a live snapshot. It doesn't explicitly state how 'query' filters results, but that's covered in the schema. The description is complete enough for the agent to use 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?
Schema description coverage is 100% (both 'limit' and 'query' have descriptions in the schema). The tool description adds no parameter-specific guidance, relying entirely on the schema. Per the rubric, the baseline is 3 when schema coverage is high, and this fits.
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 action ('List the roles Genpio is currently hiring for') with specific resource ('roles') and scope ('currently hiring for'). It enumerates the returned fields, making it distinct from sibling tools like list_avatars or service_status, which are obviously unrelated.
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: use this to see current open roles with their details. It doesn't explicitly mention alternatives or exclusions, but the sibling tools are distinctly different, so no alternative mapping is necessary. This aligns with 'clear context, no exclusions'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Genpio's public voice catalogue and filter it by language or gender. Returns matching voices with DisplayName, ShortName, Gender, Locale and LocaleName, plus the total number of matches. Every voice is one of Genpio's own multilingual models, trained by Genpio and synthesized on Genpio's own GPUs, so Locale marks a voice's primary language rather than a limit. Genpio Voices is trained from scratch in-house on the Apache-2.0 OmniVoice architecture over Qwen3-0.6B, not fine-tuned from any provider checkpoint; Genpio owns the weights it trained, holds full commercial rights, rents no model from any provider, and runs them on its own GPUs, with the model card published at https://huggingface.co/GuidenAI/genpio_voice. The training corpora are published: OpenSLR SLR94, SLR37, SLR41-44, SLR63-66, SLR78-80, SLR86, SLR129 and SLR33, plus Google FLEURS, more than 85 languages in the mix, trained together in a single run. Genpio resells no third-party TTS. Private customer voice clones are never listed.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many voices to return. The full number of matches comes back as `matched` either way, so read that rather than counting the array. | |
| gender | No | Omit to include both. | |
| language | No | Language to filter on, as a code ("vi") or a language name ("Vietnamese"). A bare code also matches its regional variants, so "vi" finds "vi-VN". Omit to search every language. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly/idempotent/destructive hints, so the description earns full credit for additional behavioral context: 'Locale marks a voice's primary language rather than a limit', 'Private customer voice clones are never listed', and 'Genpio resells no third-party TTS'. These disclosures tell an agent important things it could not infer from the annotations — that all listed voices have certain licensing characteristics and that the list intentionally excludes certain content.
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 sentence, and the Locale caveat is valuable. However, the second half contains a very long passage about the model architecture (Qwen3-0.6B, OmniVoice Apache-2.0), training corpus IDs (SLR37, SLR41-44, etc.), and redelaration that Genpio uses its own GPUs — this repeats 'Genpio's own GPUs' twice and gives more detail than an agent needs to call the tool. A shorter 'trained in-house, open license, see model card' would be sufficient.
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?
Though there is no output schema, the description explicitly names the return fields and the `matched` count, explains the semantics of Locale, and discloses exclusion rules (no clones, no third-party TTS). With the full schema documentation and the readOnly/idempotent annotations, there is genuinely nothing missing for an agent to decide whether and how to call this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already explains the limit parameter's behavior (returning a `matched` count rather than counting the array) and the language parameter's regional-variant matching. The description does not add parameter-level details beyond restating that language and gender filter; the schema does the heavy lifting, so the baseline of 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?
The opening sentence names a specific verb and resource: 'Search Genpio's public voice catalogue' and the filters (language, gender) that define the tool's scope. It also enumerates the exact return fields (DisplayName, ShortName, Gender, Locale, LocaleName, total) so an agent knows exactly what this tool produces. The focus on voices clearly differentiates it from siblings like list_avatars, list_open_roles, and service_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?
The description establishes clear context: use this tool when a voice needs to be searched or filtered from the public catalogue. The explicit mention that only public voices are returned, and the wording 'Search Genpio's public voice catalogue', imply when it should not be used (e.g., for avatars/roles/status). It does not explicitly name alternatives or state when-not conditions, but sibling tool names make the decision boundary obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report whether Genpio is up right now, component by component, covering the public API, the AI agent backend and the Meet streaming backend. Includes uptime and a timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already convey readOnly, idempotent, non-destructive behavior. The description goes further by disclosing the report's granularity (component by component) and the presence of uptime and timestamp information, which is useful behavioral and output context 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?
The description is a single focused sentence that front-loads the main action and then efficiently enumerates scope and return contents. It has no filler, repetition, or unnecessary detail.
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 zero-input, read-only status tool, the description covers enough context: monitored components, uptime, and timestamp. It could optionally mention the shape of the status field, but nothing critical is missing for selecting or invoking this 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?
The tool has zero parameters, so the input schema is complete at 100% coverage and there is no need for parameter documentation. The description still adds value by clarifying what the returned report will contain.
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 clear verb and resource: it says exactly what it does: 'Report whether Genpio is up right now.' It also names the components covered (public API, AI agent backend, Meet streaming backend), which distinguishes it from sibling tools like genpio_overview and the list_* 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?
The description clearly implies the tool is for real-time status checks of Genpio's components. It does not explicitly contrast it with genpio_overview or list_* tools, but the context is clear enough for a no-parameter status tool.
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.
genpio_overviewlist_avatarslist_open_roleslist_voicesservice_statusClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool has a clearly distinct resource: company overview, avatars, open roles, voices, and service status. There is no meaningful overlap between list_avatars and list_voices because the underlying category types are separate and described separately.
Three tools use the list_* pattern, but genpio_overview and service_status break the verb_noun convention. The naming is still readable, but the set mixes verb-led and noun-led conventions.
Five tools is a compact, well-scoped count for a read-only informational server about Genpio. Each tool adds a distinct resource without redundancy, so the agent's tool-selection space is clean and manageable.
The server covers the obvious public-facing information: product overview, avatar portfolio, voice portfolio, hiring, and system status. However, the server name emphasizes livestream, yet there are no livestream scheduling or meeting streaming tools, which is a notable gap for that stated focus.