WizerAPI Review Intelligence
Server Details
Analyze customer reviews: sentiment, emotion, topics, themes, digests, and drafted replies.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.7/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose: single review analysis (analyze_review), reply drafting (draft_reply), batch theme extraction (extract_themes), batch summary (summarize_reviews), and usage checking (check_usage). Even where functionality might overlap (e.g., analyze_review can suggest a reply), tool descriptions explicitly guide agents to avoid duplication.
All tool names follow a consistent verb_noun pattern using snake_case (analyze_review, check_usage, draft_reply, extract_themes, summarize_reviews). There are no deviations or mixed casing.
With 5 tools, the server is well-scoped for its purpose of review intelligence. Each tool serves a distinct and necessary function without redundancy or missing core operations, fitting the typical range of 3-15 tools.
The tool set covers single review analysis and reply generation, batch theme extraction and summarization, and usage monitoring. Missing capabilities like cross-review comparison or data export are minor gaps that do not hinder the primary analysis workflow.
Available Tools
5 toolsanalyze_reviewAnalyze one reviewAInspect
Analyze ONE customer review and get structured JSON: sentiment (score -1..1, label, confidence), dominant emotion, topics, detected language, and optionally a suggested reply (set options.suggest_reply, pick options.reply_tone: professional | friendly | apologetic | concise). Input: review text up to 10,000 chars, optional 0-5 rating and source. METERED: costs 1 AU + 1 AU per 4,000 input chars (a 300-char review ≈ 1.08 AU); byte-identical repeat input is served from cache for 0 AU. Every result reports its au_cost. For many reviews, prefer extract_themes/summarize_reviews over looping this tool one review at a time.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| rating | No | ||
| source | No | ||
| options | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses metering cost (1 AU + variable), caching behavior, and that every result reports au_cost. No annotations exist, so description fully covers behavioral traits.
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?
Single dense paragraph that front-loads purpose and output, then details parameters and cost. Every sentence adds value with 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?
Covers input limits, options, cost, caching, and output fields (sentiment, emotion, topics, language, reply). Lacks deeper structure details (e.g., topics format) but is sufficient given no output schema.
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?
Despite 0% schema description coverage, the description explains text (max 10,000 chars), options (suggest_reply, reply_tone enum values), and optional rating/source. It adds meaning beyond the bare 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 title 'Analyze one review' and description clearly state it analyzes a single customer review and returns structured JSON. It distinguishes from siblings by explicitly recommending extract_themes/summarize_reviews for multiple reviews.
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 guidance: 'For many reviews, prefer extract_themes/summarize_reviews over looping this tool one review at a time.' Also mentions caching for byte-identical repeats, helping the agent decide when to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_usageCheck usage & quotaAInspect
Check the current billing period usage for YOUR WizerAPI organization: plan, included AU, used AU, remaining AU, overage so far, and the per-minute rate limit. Costs 0 AU and is never blocked by the session budget. Call this BEFORE large metered jobs (theme extraction over many reviews, long contracts) to confirm quota headroom, and AFTER quota errors to see where you stand.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes that it costs 0 AU and is never blocked by session budget. With no annotations, this provides essential behavioral info. No mention of auth, but sufficient for a simple read-only 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?
Three sentences, each serving a purpose: what it does, behavioral trait, usage advice. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lists all output fields, gives context for usage. No ambiguities; sibling tools are unrelated, making this standalone.
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?
No parameters, so schema covers everything. Description adds no param info but baseline for 0 params is 4.
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 checks billing usage, listing all returned fields. It distinguishes from sibling tools which deal with reviews and themes.
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 tells when to call (before large jobs, after quota errors). Lacks when-not-to or alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
draft_replyDraft a reply to a reviewAInspect
Draft a customer-facing reply to ONE review in a chosen tone (professional | friendly | apologetic | concise, default professional). Input: review text up to 10,000 chars, optional 0-5 rating for context. Returns the reply text and tone. METERED: costs 1 AU + 1 AU per 4,000 input chars (~1 AU for a short review). Every result reports its au_cost. If you also need sentiment/topics for the same review, call analyze_review with options.suggest_reply instead of calling both tools.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| tone | No | professional | |
| rating | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses metering cost (1 AU + per 4k chars), input limit (10k chars), returns reply text and tone, and that every result reports au_cost. No hidden side effects.
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?
Two sentences plus one warning; front-loaded with core purpose. Every sentence adds value without 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?
Covers input constraints, metering, output, and alternative tool usage. No gaps given the simple parameter set and no output schema.
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?
Adds significant meaning beyond schema: explains text as review text up to 10k chars, tone options with default, and rating as optional context. Schema has 0% coverage, so description fully compensates.
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?
Explicitly states it drafts a reply to one review with tone options. Differentiates from sibling analyze_review by noting that analyze_review can suggest a reply.
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 clear guidance on when to use analyze_review instead if sentiment/topics are also needed. Also explains metering cost and tone selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_themesExtract themes from a review setAInspect
Cluster a SET of reviews into recurring themes, each with a label, frequency, theme sentiment, and example quotes. Input: 1-1,000 review strings, each up to 2,000 chars; optional max_themes (1-50, default 8). Use this ONE call instead of looping analyze_review when you need patterns across many reviews. METERED: costs 4 AU + 1 AU per 1,250 total input chars (100 reviews × 300 chars ≈ 28 AU) — check_usage first for large sets. Every result reports its au_cost.
| Name | Required | Description | Default |
|---|---|---|---|
| reviews | Yes | ||
| max_themes | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It details metered cost (4 AU + 1 AU per 1,250 chars) and that every result reports au_cost. However, it does not disclose error handling, permissions, or whether the tool is read-only (likely safe).
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 four sentences: purpose, parameter constraints, usage advice, and cost. It is front-loaded with the core action, no wasted words, and efficiently conveys all needed 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?
Given no output schema and no annotations, the description adequately covers purpose, input constraints, usage guidance, and cost. It briefly mentions output structure (label, frequency, sentiment, quotes) but omits error handling or edge cases. Nearly complete for the tool's complexity.
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 fully explain parameters. It explains reviews array (1-1000 items, up to 2000 chars) and max_themes (1-50, default 8) with ranges and defaults, adding context beyond the schema's structural constraints.
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 verb 'cluster' and resource 'reviews into recurring themes', listing outputs (label, frequency, sentiment, quotes). It distinguishes from sibling tool analyze_review by explicitly advising to use this one call instead of looping.
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 usage context: 'Use this ONE call instead of looping analyze_review when you need patterns across many reviews' and advises to 'check_usage first for large sets', offering clear when-to-use and when-not-to guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
summarize_reviewsExecutive summary of a review setAInspect
Produce an executive digest of a review SET: headline, highlights, lowlights, and recommended actions. Input: 1-1,000 review strings, each up to 2,000 chars; optional audience (default "product_team") to slant the writeup. Use for leadership-ready takeaways; use extract_themes when you need per-theme frequencies instead. METERED: costs 6 AU + 1 AU per 1,250 total input chars (50 reviews × 300 chars ≈ 18 AU) — check_usage first for large sets. Every result reports its au_cost.
| Name | Required | Description | Default |
|---|---|---|---|
| reviews | Yes | ||
| audience | No | product_team |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses metered cost model and that every result reports au_cost, which are key behavioral traits. Lacks explicit read-only or idempotency statement, but cost disclosure is substantial.
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?
Description is compact with no wasted words. Each sentence adds value: output components, input constraints, usage comparison, cost details, and result reporting. Well-structured for quick parsing.
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 no output schema, description gives a good sense of output components (headline, highlights, lowlights, actions) and cost details. Could be slightly more precise about return format, but adequate for a summarization 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 has 0% description coverage. Description adds meaning by explaining review constraints (1-1000, up to 2000 chars), audience parameter purpose and default, and that it slants the writeup. This compensates for lack of 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?
Description explicitly states it produces an executive digest of a review set with headline, highlights, lowlights, and recommended actions. It distinguishes from sibling extract_themes by noting when to use each, providing clear purpose differentiation.
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 ('leadership-ready takeaways') and alternatives ('use extract_themes when you need per-theme frequencies'). Also provides cost guidance and recommends checking usage first for large sets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!